From 99386bf5417c1ff844fc77077b3ba48696b64a7c Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 25 Jun 2024 13:48:21 -0400 Subject: [PATCH 001/106] begin implementation of BaseDataIOIterator --- benchmarks/MOABB/utils/dataio_iterators.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/benchmarks/MOABB/utils/dataio_iterators.py b/benchmarks/MOABB/utils/dataio_iterators.py index 07379f198..8c0c91f0c 100644 --- a/benchmarks/MOABB/utils/dataio_iterators.py +++ b/benchmarks/MOABB/utils/dataio_iterators.py @@ -128,7 +128,14 @@ def sample_channels(x, adjacency_mtx, ch_names, n_steps, seed_nodes=["Cz"]): return x -class LeaveOneSessionOut(object): +class BaseDataIOIterator: + + def __init__(self, tag, seed): + self.iterator_tag = tag + np.random.seed(seed) + + +class LeaveOneSessionOut(BaseDataIOIterator): """Leave one session out iterator for MOABB datasets. Designing within-subject, cross-session and session-agnostic iterations on the dataset for a specific paradigm. For each subject, one session is held back as test set and the remaining ones are used to train neural networks. @@ -142,8 +149,7 @@ class LeaveOneSessionOut(object): """ def __init__(self, seed): - self.iterator_tag = "leave-one-session-out" - np.random.seed(seed) + super().__init__("leave-one-session-out", seed) def prepare( self, @@ -347,7 +353,7 @@ def prepare( return tail_path, datasets -class LeaveOneSubjectOut(object): +class LeaveOneSubjectOut(BaseDataIOIterator): """Leave one subject out iterator for MOABB datasets. Designing cross-subject, cross-session and subject-agnostic iterations on the dataset for a specific paradigm. One subject is held back as test set and the remaining ones are used to train neural networks. @@ -361,8 +367,7 @@ class LeaveOneSubjectOut(object): """ def __init__(self, seed): - self.iterator_tag = "leave-one-subject-out" - np.random.seed(seed) + super().__init__("leave-one-subject-out", seed) def prepare( self, From 04b15244babcfaf99016b35834a54e963fc58c70 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 25 Jun 2024 15:42:47 -0400 Subject: [PATCH 002/106] refactor common logic to baseDataIOIterator --- benchmarks/MOABB/utils/dataio_iterators.py | 519 ++++++++++----------- 1 file changed, 237 insertions(+), 282 deletions(-) diff --git a/benchmarks/MOABB/utils/dataio_iterators.py b/benchmarks/MOABB/utils/dataio_iterators.py index 8c0c91f0c..e3ce6095e 100644 --- a/benchmarks/MOABB/utils/dataio_iterators.py +++ b/benchmarks/MOABB/utils/dataio_iterators.py @@ -11,10 +11,16 @@ Davide Borra, 2021 """ +import abc +import os +from collections import namedtuple +from typing import List, Optional, Tuple, TypedDict + import numpy as np +import pandas as pd import torch -from torch.utils.data import TensorDataset, DataLoader -import os +from moabb.datasets.base import BaseDataset as MOABBDataset +from torch.utils.data import DataLoader, TensorDataset from utils.prepare import prepare_data @@ -128,48 +134,50 @@ def sample_channels(x, adjacency_mtx, ch_names, n_steps, seed_nodes=["Cz"]): return x -class BaseDataIOIterator: - - def __init__(self, tag, seed): - self.iterator_tag = tag - np.random.seed(seed) +class _SplitDataloaders(TypedDict): + train: DataLoader + valid: DataLoader + test: DataLoader -class LeaveOneSessionOut(BaseDataIOIterator): - """Leave one session out iterator for MOABB datasets. - Designing within-subject, cross-session and session-agnostic iterations on the dataset for a specific paradigm. - For each subject, one session is held back as test set and the remaining ones are used to train neural networks. - The validation set can be sampled from a separate (and held back) session if enough sessions are available; otherwise, the validation set is sampled from the training set. - All sets are extracted balanced across subjects, sessions and classes. +XYSplits = namedtuple( + "XYSplits", ["x_train", "y_train", "x_valid", "y_valid", "x_test", "y_test"] +) - Arguments - --------- - seed: int - Seed for random number generators. - """ - def __init__(self, seed): - super().__init__("leave-one-session-out", seed) +class _DataDict(TypedDict): + srate: int + original_interval: Tuple[float, float] + adjacency_mtx: np.ndarray + channels: List[str] + subject: str + + +class BaseDataIOIterator(abc.ABC): + def __init__(self, tag, seed): + self.iterator_tag = tag + np.random.seed(seed) def prepare( self, - data_folder=None, - cached_data_folder=None, - dataset=None, - batch_size=None, - valid_ratio=None, - target_subject_idx=None, - target_session_idx=None, - events_to_load=None, - original_sample_rate=None, - sample_rate=None, - fmin=None, - fmax=None, - tmin=None, - tmax=None, - save_prepared_dataset=None, - n_steps_channel_selection=None, - ): + *, + data_folder: str, + cached_data_folder: str, + dataset: MOABBDataset, + batch_size: int, + valid_ratio: float, + original_sample_rate: int, + target_subject_idx: int, + target_session_idx: Optional[int] = None, + sample_rate: Optional[int] = None, + fmin: Optional[float] = None, + fmax: Optional[float] = None, + tmin: Optional[float] = None, + tmax: Optional[float] = None, + n_steps_channel_selection: Optional[int] = None, + events_to_load: Optional[List[str]] = None, + save_prepared_dataset: bool = True, + ) -> tuple[str, dict[str, _SplitDataloaders]]: """This function returns the pre-processed datasets (training, validation and test sets). Arguments @@ -186,16 +194,12 @@ def prepare( Mini-batch size. valid_ratio: float Ratio for extracting the validation set from the available training set (between 0 and 1). + original_sample_rate: int + Sampling rate of the loaded dataset (Hz). target_subject_idx: int Index of the subject to use to train the network. target_session_idx: int Index of the session to use to train the network. - events_to_load: list - List of 'events' considered when loading the MOABB dataset. - It serves to load specific conditions (e.g., ["right_hand", "left_hand"] for specific movement conditions). - See MOABB documentation and reference publications of each dataset for additional details about datasets. - original_sample_rate: int - Sampling rate of the loaded dataset (Hz). sample_rate: int Target sampling rate (Hz). fmin: float @@ -208,10 +212,15 @@ def prepare( tmax: float Stop time of the EEG epoch, with respect to the event as defined in the dataset (s). See MOABB documentation and reference publications of each dataset for additional details about datasets. - save_prepared_dataset: bool - Flag to save the prepared dataset into a pkl file. n_steps_channel_selection: int Number of steps to perform when sampling a subset of channels from a seed channel, based on the adjacency matrix. + events_to_load: list + List of 'events' considered when loading the MOABB dataset. + It serves to load specific conditions (e.g., ["right_hand", "left_hand"] for specific movement conditions). + See MOABB documentation and reference publications of each dataset for additional details about datasets. + + save_prepared_dataset: bool + Flag to save the prepared dataset into a pkl file. ... Returns @@ -219,93 +228,50 @@ def prepare( tail_path: str String containing the relative path where results will be stored for the specified iterator, subject and session. datasets: dict - Dictionary containing all sets (keys: 'train', 'test', 'valid'). - --------- + Dictionary containing all sets as dataloaders (keys: 'train', 'test', 'valid'). + --------- """ interval = [tmin, tmax] + subject = dataset.subject_list[target_subject_idx] - # preparing or loading dataset - data_dict = prepare_data( + self.validate_dataset_or_raise(dataset) + + ( + target_data_dict, + (x_train, y_train, x_valid, y_valid, x_test, y_test), + ) = self.get_data( + target_subject_idx=target_subject_idx, + target_session_idx=target_session_idx, + valid_ratio=valid_ratio, + dataset=dataset, data_folder=data_folder, cached_data_folder=cached_data_folder, - dataset=dataset, - events_to_load=events_to_load, - srate_in=original_sample_rate, - srate_out=sample_rate, + original_sample_rate=original_sample_rate, + sample_rate=sample_rate, fmin=fmin, fmax=fmax, - idx_subject_to_prepare=target_subject_idx, + events_to_load=events_to_load, save_prepared_dataset=save_prepared_dataset, ) - x = data_dict["x"] - y = data_dict["y"] - srate = data_dict["srate"] - original_interval = data_dict["interval"] - metadata = data_dict["metadata"] - if np.unique(metadata.session).shape[0] < 2: - raise ( - ValueError( - "The number of sessions in the dataset must be >= 2 for leave-one-session-out iterations" - ) - ) - sessions = np.unique(metadata.session) - sess_id_test = [sessions[target_session_idx]] - sess_id_train = np.setdiff1d(sessions, sess_id_test) - sess_id_train = list(sess_id_train) - print( - "Session/sessions used as training and validation set: {0}".format( - sess_id_train - ) - ) - print("Session used as test set: {0}".format(sess_id_test)) - # iterate over sessions to accumulate session train and valid examples in a balanced way across sessions - idx_train, idx_valid = [], [] - for s in sess_id_train: - # obtaining indices for the current session - idx = np.where(metadata.session == s)[0] - # validation set definition (equal proportion btw classes) - (tmp_idx_train, tmp_idx_valid,) = get_idx_train_valid_classbalanced( - idx, valid_ratio, y - ) - idx_train.extend(tmp_idx_train) - idx_valid.extend(tmp_idx_valid) - - idx_test = [] - for s in sess_id_test: - # obtaining indices for the current session - idx = np.where(metadata.session == s)[0] - idx_test.extend(idx) - - idx_train = np.array(idx_train) - idx_valid = np.array(idx_valid) - idx_test = np.array(idx_test) - - x_train = x[idx_train, ...] - y_train = y[idx_train] - x_valid = x[idx_valid, ...] - y_valid = y[idx_valid] - x_test = x[idx_test, ...] - y_test = y[idx_test] - # time cropping - if interval != original_interval: + if interval != target_data_dict["original_interval"]: x_train = crop_signals( x=x_train, - srate=srate, - interval_in=original_interval, + srate=target_data_dict["srate"], + interval_in=target_data_dict["original_interval"], interval_out=interval, ) x_valid = crop_signals( x=x_valid, - srate=srate, - interval_in=original_interval, + srate=target_data_dict["srate"], + interval_in=target_data_dict["original_interval"], interval_out=interval, ) x_test = crop_signals( x=x_test, - srate=srate, - interval_in=original_interval, + srate=target_data_dict["srate"], + interval_in=target_data_dict["original_interval"], interval_out=interval, ) @@ -313,20 +279,20 @@ def prepare( if n_steps_channel_selection is not None: x_train = sample_channels( x_train, - data_dict["adjacency_mtx"], - data_dict["channels"], + target_data_dict["adjacency_mtx"], + target_data_dict["channels"], n_steps=n_steps_channel_selection, ) x_valid = sample_channels( x_valid, - data_dict["adjacency_mtx"], - data_dict["channels"], + target_data_dict["adjacency_mtx"], + target_data_dict["channels"], n_steps=n_steps_channel_selection, ) x_test = sample_channels( x_test, - data_dict["adjacency_mtx"], - data_dict["channels"], + target_data_dict["adjacency_mtx"], + target_data_dict["channels"], n_steps=n_steps_channel_selection, ) @@ -343,14 +309,140 @@ def prepare( datasets["train"] = train_loader datasets["valid"] = valid_loader datasets["test"] = test_loader + + tail_path = self.get_tail_path(subject, target_data_dict["session"]) + return tail_path, datasets + + def validate_dataset_or_raise(self, dataset: MOABBDataset) -> None: + """Check that the dataset is compatible, or raise an error.""" + pass + + @abc.abstractmethod + def get_data( + self, + target_subject_idx: int, + target_session_idx: Optional[int], + valid_ratio: float, + dataset: MOABBDataset, + data_folder: Optional[str], + cached_data_folder: Optional[str], + original_sample_rate: int, + sample_rate: Optional[int], + fmin: Optional[float], + fmax: Optional[float], + events_to_load: Optional[List[str]], + save_prepared_dataset: bool, + ) -> tuple[_DataDict, XYSplits]: + """Prepare the numpy data as train, valid and test splits. + + Arguments + ========= + See `prepare` method. + + Returns + ======= + target_data_dict: _DataDict + The metadata relating to the target, including session name and + adjacency matrix. + (x_train, y_train, x_valid, y_valid, x_test, y_test): np.ndarray + The numpy arrays corresponding to each data split. + """ + + def get_tail_path(self, subject: int, session: Optional[str]) -> str: + """Returns the tail path for the directory.""" + tail_path = os.path.join( - self.iterator_tag, - "sub-{0}".format( - str(dataset.subject_list[target_subject_idx]).zfill(3) - ), - sessions[target_session_idx], + self.iterator_tag, "sub-{0}".format(str(subject).zfill(3)), ) - return tail_path, datasets + if session is not None: + tail_path = os.path.join(tail_path, session) + return tail_path + + +class LeaveOneSessionOut(BaseDataIOIterator): + """Leave one session out iterator for MOABB datasets. + Designing within-subject, cross-session and session-agnostic iterations on the dataset for a specific paradigm. + For each subject, one session is held back as test set and the remaining ones are used to train neural networks. + The validation set can be sampled from a separate (and held back) session if enough sessions are available; otherwise, the validation set is sampled from the training set. + All sets are extracted balanced across subjects, sessions and classes. + + Arguments + --------- + seed: int + Seed for random number generators. + """ + + def __init__(self, seed): + super().__init__("leave-one-session-out", seed) + + def get_data( + self, + target_subject_idx, + target_session_idx, + valid_ratio, + **prepare_data_kwargs, + ) -> Tuple[dict, XYSplits]: + # preparing or loading dataset + data_dict = prepare_data( + idx_subject_to_prepare=target_subject_idx, **prepare_data_kwargs, + ) + + x = data_dict["x"] + y = data_dict["y"] + metadata = data_dict["metadata"] + + self._validate_metadata_or_raise(metadata) + sessions = np.unique(metadata.session) + sess_id_test = [sessions[target_session_idx]] + sess_id_train = np.setdiff1d(sessions, sess_id_test) + sess_id_train = list(sess_id_train) + print( + "Session/sessions used as training and validation set: {0}".format( + sess_id_train + ) + ) + print("Session used as test set: {0}".format(sess_id_test)) + # iterate over sessions to accumulate session train and valid examples in a balanced way across sessions + idx_train, idx_valid = [], [] + for s in sess_id_train: + # obtaining indices for the current session + idx = np.where(metadata.session == s)[0] + # validation set definition (equal proportion btw classes) + (tmp_idx_train, tmp_idx_valid,) = get_idx_train_valid_classbalanced( + idx, valid_ratio, y + ) + idx_train.extend(tmp_idx_train) + idx_valid.extend(tmp_idx_valid) + + idx_test = [] + for s in sess_id_test: + # obtaining indices for the current session + idx = np.where(metadata.session == s)[0] + idx_test.extend(idx) + + idx_train = np.array(idx_train) + idx_valid = np.array(idx_valid) + idx_test = np.array(idx_test) + + x_train = x[idx_train, ...] + y_train = y[idx_train] + x_valid = x[idx_valid, ...] + y_valid = y[idx_valid] + x_test = x[idx_test, ...] + y_test = y[idx_test] + + return ( + data_dict, + XYSplits(x_train, y_train, x_valid, y_valid, x_test, y_test), + ) + + def _validate_metadata_or_raise(self, metadata: pd.DataFrame): + if np.unique(metadata.session).shape[0] < 2: + raise ( + ValueError( + "The number of sessions in the dataset must be >= 2 for leave-one-session-out iterations" + ) + ) class LeaveOneSubjectOut(BaseDataIOIterator): @@ -369,105 +461,24 @@ class LeaveOneSubjectOut(BaseDataIOIterator): def __init__(self, seed): super().__init__("leave-one-subject-out", seed) - def prepare( + def get_data( self, - data_folder=None, - cached_data_folder=None, - dataset=None, - batch_size=None, - valid_ratio=None, + *, + dataset: MOABBDataset, target_subject_idx=None, - target_session_idx=None, - events_to_load=None, - original_sample_rate=None, - sample_rate=None, - fmin=None, - fmax=None, - tmin=None, - tmax=None, - save_prepared_dataset=None, - n_steps_channel_selection=None, + target_session_idx=None, # noqa + valid_ratio=None, + **prepare_data_kwargs, ): - """This function returns the pre-processed datasets (training, validation and test sets). - - Arguments - --------- - data_folder: str - String containing the path where data were downloaded. - cached_data_folder: str - String containing the path where data will be cached (into .pkl files) during preparation. - This is convenient to speed up overall training when performing multiple trainings on EEG signals - pre-processed always in the same way. - dataset: moabb.datasets.? - MOABB dataset. - batch_size: int - Mini-batch size. - valid_ratio: float - Ratio for extracting the validation set from the available training set (between 0 and 1). - target_subject_idx: int - Index of the subject to use to train the network. - target_session_idx: int - Index of the session to use to train the network. - For leave-one-subject-out this parameter is ininfluent (it is not used). - It was held for symmetry with leave-one-session-out iterator and for convenience with the rest of the code. - events_to_load: list - List of 'events' considered when loading the MOABB dataset. - It serves to load specific conditions (e.g., ["right_hand", "left_hand"] for specific movement conditions). - See MOABB documentation and reference publications of each dataset for additional details about datasets. - original_sample_rate: int - Sampling rate of the loaded dataset (Hz). - sample_rate: int - Target sampling rate (Hz). - fmin: float - Low cut-off frequency of the applied band-pass filtering (Hz). - fmax: float - High cut-off frequency of the applied band-pass filtering (Hz). - tmin: float - Start time of the EEG epoch, with respect to the event as defined in the dataset (s). - See MOABB documentation and reference publications of each dataset for additional details about datasets. - tmax: float - Stop time of the EEG epoch, with respect to the event as defined in the dataset (s). - See MOABB documentation and reference publications of each dataset for additional details about datasets. - save_prepared_dataset: bool - Flag to save the prepared dataset into a pkl file. - n_steps_channel_selection: int - Number of steps to perform when sampling a subset of channels from a seed channel, based on the adjacency matrix. - ... - - Returns - --------- - tail_path: str - String containing the relative path where results will be stored for the specified iterator, subject and session. - datasets: dict - Dictionary containing all sets (keys: 'train', 'test', 'valid'). - --------- - """ - interval = [tmin, tmax] - if len(dataset.subject_list) < 2: - raise ( - ValueError( - "The number of subjects in the dataset must be >= 2 for leave-one-subject-out iterations" - ) - ) - # preparing or loading test set - data_dict = prepare_data( - data_folder=data_folder, - cached_data_folder=cached_data_folder, + target_data_dict = prepare_data( dataset=dataset, - events_to_load=events_to_load, - srate_in=original_sample_rate, - srate_out=sample_rate, - fmin=fmin, - fmax=fmax, idx_subject_to_prepare=target_subject_idx, - save_prepared_dataset=save_prepared_dataset, + **prepare_data_kwargs, ) - x_test = data_dict["x"] - y_test = data_dict["y"] - original_interval = data_dict["interval"] - srate = data_dict["srate"] + x_test = target_data_dict["x"] + y_test = target_data_dict["y"] subject_idx_train = [ i @@ -493,16 +504,9 @@ def prepare( for subject_idx in subject_idx_train: # preparing or loading training/valid set data_dict = prepare_data( - data_folder=data_folder, - cached_data_folder=cached_data_folder, dataset=dataset, - events_to_load=events_to_load, - srate_in=original_sample_rate, - srate_out=sample_rate, - fmin=fmin, - fmax=fmax, idx_subject_to_prepare=subject_idx, - save_prepared_dataset=save_prepared_dataset, + **prepare_data_kwargs, ) tmp_x_train = data_dict["x"] @@ -534,70 +538,21 @@ def prepare( y_train.extend(tmp_y_train) x_valid.extend(tmp_x_valid) y_valid.extend(tmp_y_valid) + x_train = np.array(x_train) y_train = np.array(y_train) x_valid = np.array(x_valid) y_valid = np.array(y_valid) - # time cropping - if interval != original_interval: - x_train = crop_signals( - x=x_train, - srate=srate, - interval_in=original_interval, - interval_out=interval, - ) - x_valid = crop_signals( - x=x_valid, - srate=srate, - interval_in=original_interval, - interval_out=interval, - ) - x_test = crop_signals( - x=x_test, - srate=srate, - interval_in=original_interval, - interval_out=interval, - ) + return ( + target_data_dict, + (x_train, y_train, x_valid, y_valid, x_test, y_test,), + ) - # channel sampling - if n_steps_channel_selection is not None: - x_train = sample_channels( - x_train, - data_dict["adjacency_mtx"], - data_dict["channels"], - n_steps=n_steps_channel_selection, - ) - x_valid = sample_channels( - x_valid, - data_dict["adjacency_mtx"], - data_dict["channels"], - n_steps=n_steps_channel_selection, - ) - x_test = sample_channels( - x_test, - data_dict["adjacency_mtx"], - data_dict["channels"], - n_steps=n_steps_channel_selection, + def validate_dataset_or_raise(self, dataset: MOABBDataset): + if len(dataset.subject_list) < 2: + raise ( + ValueError( + "The number of subjects in the dataset must be >= 2 for leave-one-subject-out iterations" + ) ) - - # swap axes: from (N_examples, C, T) to (N_examples, T, C) - x_train = np.swapaxes(x_train, -1, -2) - x_valid = np.swapaxes(x_valid, -1, -2) - x_test = np.swapaxes(x_test, -1, -2) - - # dataloaders - train_loader, valid_loader, test_loader = get_dataloader( - batch_size, (x_train, y_train), (x_valid, y_valid), (x_test, y_test) - ) - datasets = {} - datasets["train"] = train_loader - datasets["valid"] = valid_loader - datasets["test"] = test_loader - tail_path = os.path.join( - self.iterator_tag, - "sub-{0}".format( - str(dataset.subject_list[target_subject_idx]).zfill(3) - ), - ) - return tail_path, datasets From c62cfd04370cb8f145ac4b83be7ca83a6dc2b3f1 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 25 Jun 2024 15:47:13 -0400 Subject: [PATCH 003/106] update authors --- benchmarks/MOABB/utils/dataio_iterators.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/benchmarks/MOABB/utils/dataio_iterators.py b/benchmarks/MOABB/utils/dataio_iterators.py index e3ce6095e..58ad4499b 100644 --- a/benchmarks/MOABB/utils/dataio_iterators.py +++ b/benchmarks/MOABB/utils/dataio_iterators.py @@ -6,9 +6,10 @@ - Leave-One-Session-Out - Leave-One-Subject-Out -Author +Authors ------ Davide Borra, 2021 +Drew Wagner, 2024 """ import abc From f454dfa7204f3f48505a074807acd3192b14425c Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 25 Jun 2024 15:52:36 -0400 Subject: [PATCH 004/106] fix typing error --- benchmarks/MOABB/utils/dataio_iterators.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/utils/dataio_iterators.py b/benchmarks/MOABB/utils/dataio_iterators.py index 58ad4499b..940831bc0 100644 --- a/benchmarks/MOABB/utils/dataio_iterators.py +++ b/benchmarks/MOABB/utils/dataio_iterators.py @@ -178,7 +178,7 @@ def prepare( n_steps_channel_selection: Optional[int] = None, events_to_load: Optional[List[str]] = None, save_prepared_dataset: bool = True, - ) -> tuple[str, dict[str, _SplitDataloaders]]: + ) -> Tuple[str, _SplitDataloaders]: """This function returns the pre-processed datasets (training, validation and test sets). Arguments From 39948b2b63785e3a70182954588f9c81177a32d0 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Thu, 27 Jun 2024 19:24:39 -0400 Subject: [PATCH 005/106] bug fixes and returning channel positions --- benchmarks/MOABB/utils/dataio_iterators.py | 76 +++++++++++++++------- benchmarks/MOABB/utils/prepare.py | 17 +++-- 2 files changed, 66 insertions(+), 27 deletions(-) diff --git a/benchmarks/MOABB/utils/dataio_iterators.py b/benchmarks/MOABB/utils/dataio_iterators.py index 940831bc0..566a5c720 100644 --- a/benchmarks/MOABB/utils/dataio_iterators.py +++ b/benchmarks/MOABB/utils/dataio_iterators.py @@ -15,7 +15,7 @@ import abc import os from collections import namedtuple -from typing import List, Optional, Tuple, TypedDict +from typing import Dict, List, Optional, Tuple, TypedDict import numpy as np import pandas as pd @@ -126,19 +126,21 @@ def sample_channels(x, adjacency_mtx, ch_names, n_steps, seed_nodes=["Cz"]): if idx_sel_channels.shape[0] != x.shape[1]: x = x[:, idx_sel_channels, :] - sel_channels_ = np.array(ch_names)[idx_sel_channels] - sel_channels_ = list(sel_channels_) + ch_names = np.array(ch_names)[idx_sel_channels] + ch_names = list(ch_names) # should correspond to sel_channels ordered based on ch_names ordering - print("Sampling channels: {0}".format(sel_channels_)) + print("Sampling channels: {0}".format(ch_names)) else: print("Sampling all channels available: {0}".format(ch_names)) - return x + return x, ch_names class _SplitDataloaders(TypedDict): train: DataLoader valid: DataLoader test: DataLoader + ch_positions: np.ndarray + ch_names: List[str] XYSplits = namedtuple( @@ -150,6 +152,7 @@ class _DataDict(TypedDict): srate: int original_interval: Tuple[float, float] adjacency_mtx: np.ndarray + ch_positions: Dict[str, List[float]] channels: List[str] subject: str @@ -256,41 +259,41 @@ def prepare( ) # time cropping - if interval != target_data_dict["original_interval"]: + if interval != target_data_dict["interval"]: x_train = crop_signals( x=x_train, srate=target_data_dict["srate"], - interval_in=target_data_dict["original_interval"], + interval_in=target_data_dict["interval"], interval_out=interval, ) x_valid = crop_signals( x=x_valid, srate=target_data_dict["srate"], - interval_in=target_data_dict["original_interval"], + interval_in=target_data_dict["interval"], interval_out=interval, ) x_test = crop_signals( x=x_test, srate=target_data_dict["srate"], - interval_in=target_data_dict["original_interval"], + interval_in=target_data_dict["interval"], interval_out=interval, ) # channel sampling if n_steps_channel_selection is not None: - x_train = sample_channels( + x_train, ch_names = sample_channels( x_train, target_data_dict["adjacency_mtx"], target_data_dict["channels"], n_steps=n_steps_channel_selection, ) - x_valid = sample_channels( + x_valid, _ = sample_channels( x_valid, target_data_dict["adjacency_mtx"], target_data_dict["channels"], n_steps=n_steps_channel_selection, ) - x_test = sample_channels( + x_test, _ = sample_channels( x_test, target_data_dict["adjacency_mtx"], target_data_dict["channels"], @@ -306,12 +309,19 @@ def prepare( train_loader, valid_loader, test_loader = get_dataloader( batch_size, (x_train, y_train), (x_valid, y_valid), (x_test, y_test) ) - datasets = {} - datasets["train"] = train_loader - datasets["valid"] = valid_loader - datasets["test"] = test_loader - tail_path = self.get_tail_path(subject, target_data_dict["session"]) + ch_positions = target_data_dict["ch_positions"] + ch_positions = np.row_stack([ch_positions[ch] for ch in ch_names]) + + datasets: _SplitDataloaders = dict( + train=train_loader, + valid=valid_loader, + test=test_loader, + channel_positions=ch_positions, + ch_names=ch_names, + ) + + tail_path = self.get_tail_path(subject, target_data_dict.get("session")) return tail_path, datasets def validate_dataset_or_raise(self, dataset: MOABBDataset) -> None: @@ -378,14 +388,31 @@ def __init__(self, seed): def get_data( self, - target_subject_idx, - target_session_idx, - valid_ratio, - **prepare_data_kwargs, + target_subject_idx: int, + target_session_idx: Optional[int], + valid_ratio: float, + dataset: MOABBDataset, + data_folder: Optional[str], + cached_data_folder: Optional[str], + original_sample_rate: int, + sample_rate: Optional[int], + fmin: Optional[float], + fmax: Optional[float], + events_to_load: Optional[List[str]], + save_prepared_dataset: bool, ) -> Tuple[dict, XYSplits]: # preparing or loading dataset data_dict = prepare_data( - idx_subject_to_prepare=target_subject_idx, **prepare_data_kwargs, + idx_subject_to_prepare=target_subject_idx, + dataset=dataset, + data_folder=data_folder, + cached_data_folder=cached_data_folder, + srate_in=original_sample_rate, + srate_out=sample_rate, + fmin=fmin, + fmax=fmax, + save_prepared_dataset=save_prepared_dataset, + events_to_load=events_to_load, ) x = data_dict["x"] @@ -394,7 +421,10 @@ def get_data( self._validate_metadata_or_raise(metadata) sessions = np.unique(metadata.session) - sess_id_test = [sessions[target_session_idx]] + + data_dict["session"] = sessions[target_session_idx] + + sess_id_test = [data_dict["session"]] sess_id_train = np.setdiff1d(sessions, sess_id_test) sess_id_train = list(sess_id_train) print( diff --git a/benchmarks/MOABB/utils/prepare.py b/benchmarks/MOABB/utils/prepare.py index 9f6371be4..b2bcbab68 100644 --- a/benchmarks/MOABB/utils/prepare.py +++ b/benchmarks/MOABB/utils/prepare.py @@ -78,9 +78,16 @@ def get_output_dict( resample=srate_out, # downsample ) - x, y, labels, metadata, channels, adjacency_mtx, srate = load_data( - paradigm, dataset, [subject] - ) + ( + x, + y, + labels, + metadata, + channels, + adjacency_mtx, + ch_positions, + srate, + ) = load_data(paradigm, dataset, [subject]) if verbose == 1: for l in np.unique(labels): @@ -106,6 +113,7 @@ def get_output_dict( output_dict["channels"] = channels output_dict["adjacency_mtx"] = adjacency_mtx + output_dict["ch_positions"] = ch_positions output_dict["x"] = x output_dict["y"] = y output_dict["labels"] = labels @@ -122,6 +130,7 @@ def load_data(paradigm, dataset, idx): In addition metadata, channel names and the sampling rate are provided too.""" x, labels, metadata = paradigm.get_data(dataset, idx, True) ch_names = x.info.ch_names + ch_positions = x.info.get_montage().get_positions()["ch_pos"] adjacency, _ = find_ch_adjacency(x.info, ch_type="eeg") adjacency_mtx = scipy.sparse.csr_matrix.toarray( adjacency @@ -132,7 +141,7 @@ def load_data(paradigm, dataset, idx): y = [dataset.event_id[yy] for yy in labels] y = np.array(y) y -= y.min() - return x, y, labels, metadata, ch_names, adjacency_mtx, srate + return x, y, labels, metadata, ch_names, adjacency_mtx, ch_positions, srate def download_data(data_folder, dataset): From aadeb29df9bbbc5343964931e7deae2e8b46455f Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Thu, 27 Jun 2024 19:37:18 -0400 Subject: [PATCH 006/106] return adjacency matrix --- benchmarks/MOABB/utils/dataio_iterators.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/benchmarks/MOABB/utils/dataio_iterators.py b/benchmarks/MOABB/utils/dataio_iterators.py index 566a5c720..83552c396 100644 --- a/benchmarks/MOABB/utils/dataio_iterators.py +++ b/benchmarks/MOABB/utils/dataio_iterators.py @@ -132,15 +132,16 @@ def sample_channels(x, adjacency_mtx, ch_names, n_steps, seed_nodes=["Cz"]): print("Sampling channels: {0}".format(ch_names)) else: print("Sampling all channels available: {0}".format(ch_names)) - return x, ch_names + return x, idx_sel_channels, ch_names class _SplitDataloaders(TypedDict): train: DataLoader valid: DataLoader test: DataLoader - ch_positions: np.ndarray ch_names: List[str] + ch_positions: np.ndarray + adjacency_mtx: np.ndarray XYSplits = namedtuple( @@ -281,19 +282,19 @@ def prepare( # channel sampling if n_steps_channel_selection is not None: - x_train, ch_names = sample_channels( + x_train, ch_idx, ch_names = sample_channels( x_train, target_data_dict["adjacency_mtx"], target_data_dict["channels"], n_steps=n_steps_channel_selection, ) - x_valid, _ = sample_channels( + x_valid, *_ = sample_channels( x_valid, target_data_dict["adjacency_mtx"], target_data_dict["channels"], n_steps=n_steps_channel_selection, ) - x_test, _ = sample_channels( + x_test, *_ = sample_channels( x_test, target_data_dict["adjacency_mtx"], target_data_dict["channels"], @@ -313,12 +314,15 @@ def prepare( ch_positions = target_data_dict["ch_positions"] ch_positions = np.row_stack([ch_positions[ch] for ch in ch_names]) + adj_mtx = target_data_dict["adjacency_mtx"][ch_idx, :][:, ch_idx] + datasets: _SplitDataloaders = dict( train=train_loader, valid=valid_loader, test=test_loader, - channel_positions=ch_positions, ch_names=ch_names, + channel_positions=ch_positions, + adjacency_mtx=adj_mtx, ) tail_path = self.get_tail_path(subject, target_data_dict.get("session")) From 541edf18febe0f019b690e518107ddd6272d2127 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Sat, 29 Jun 2024 15:45:10 -0400 Subject: [PATCH 007/106] EEGNet with spatial focus module --- .../EEGNet_with_spatial_focus.yaml | 180 ++++++++++++++++++ benchmarks/MOABB/models/SpatialFocus.py | 60 ++++++ 2 files changed, 240 insertions(+) create mode 100644 benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml create mode 100644 benchmarks/MOABB/models/SpatialFocus.py diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml new file mode 100644 index 000000000..7397a0408 --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml @@ -0,0 +1,180 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: + !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder + + +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' + + +output_folder: !PLACEHOLDER + #'path/to/results' + + # DATASET HPARS + # Defining the MOABB dataset. + + +dataset: !new:moabb.datasets.BNCI2014001 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 250 # Original sampling rate provided by dataset authors +sample_rate: 125 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.13 # @orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 4 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +# number of steps used when selecting adjacent channels from a seed channel (default at Cz) +n_steps_channel_selection: 2 # @orion_step1: --n_steps_channel_selection~"uniform(1, 3,discrete=True)" +T: !apply:math.ceil + - !ref * ( - ) +C: 22 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: "last" # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: "macro" +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 862 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" +lr: 0.0001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.01742 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 1 # orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 15.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +# MODEL +number_of_foci: 22 # @orion_step1: --number_of_foci~"uniform(4, 64, discrete=True)" +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(4, 64,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +eeg_net: !new:models.EEGNet.EEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref +spatial_focus: !new:models.SpatialFocus.SpatialFocus + n_focal_points: !ref + focus_dims: 3 +model: !apply:models.SpatialFocus.inject_spatial_focus + model: !ref + spatial_focus: !ref + after: conv_module.bnorm_0 diff --git a/benchmarks/MOABB/models/SpatialFocus.py b/benchmarks/MOABB/models/SpatialFocus.py new file mode 100644 index 000000000..df54737a8 --- /dev/null +++ b/benchmarks/MOABB/models/SpatialFocus.py @@ -0,0 +1,60 @@ +import torch +from torch import nn + + +class SpatialFocus(nn.Module): + def __init__( + self, + n_focal_points, + focus_dims=3, + similarity_func="cosine", + similarity_transform=nn.Softmax(-1), + ): + super().__init__() + self.n_focal_points = n_focal_points + self.focus_dims = focus_dims + self.similarity_func = similarity_func + self.similarity_transform = similarity_transform + + self.focal_points = nn.Embedding( + num_embeddings=n_focal_points, embedding_dim=self.focus_dims + ) + + def forward(self, x: torch.Tensor, positions: torch.Tensor): + focal_points = self.focal_points.weight + + if self.similarity_func == "cosine": + similarity = (positions @ focal_points.T) / ( + positions.norm(dim=-1).unsqueeze(1) + @ focal_points.norm(dim=-1).unsqueeze(0) + ) + else: + similarity = self.similarity_func(positions, focal_points) + + if self.similarity_transform: + weights = self.similarity_transform(similarity) + else: + weights = similarity + + weights = weights.unsqueeze(0) # Batch dimension + if x.dim() == 4: + weights = weights.unsqueeze(1) # Time dimension + weights = weights.unsqueeze(-1) # Feature dimension + + x = x.unsqueeze(-2) # Focus dimension + x = (x * weights).sum(dim=-2) + + return x + + +def inject_spatial_focus( + model: nn.Module, spatial_focus: SpatialFocus, after: str +): + seq, module_name = after.split(".") + + module: nn.Sequential = getattr(model, seq) + for idx, (name, _) in enumerate(module.named_modules()): + if name == module_name: + module.insert(idx + 1, spatial_focus) + + return model From 25bcb1ba765090dc98c7d0585a9cf62bd6c007d6 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Sat, 29 Jun 2024 16:22:15 -0400 Subject: [PATCH 008/106] bug fix --- benchmarks/MOABB/models/SpatialFocus.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/benchmarks/MOABB/models/SpatialFocus.py b/benchmarks/MOABB/models/SpatialFocus.py index df54737a8..94cb1f95a 100644 --- a/benchmarks/MOABB/models/SpatialFocus.py +++ b/benchmarks/MOABB/models/SpatialFocus.py @@ -53,8 +53,11 @@ def inject_spatial_focus( seq, module_name = after.split(".") module: nn.Sequential = getattr(model, seq) - for idx, (name, _) in enumerate(module.named_modules()): + new_module = nn.Sequential() + for idx, (name, mod) in enumerate(module.named_modules()): + new_module.append(mod) if name == module_name: - module.insert(idx + 1, spatial_focus) + new_module.append(mod) + setattr(model, seq, new_module) return model From 700a1b21e8e2b1a7dfb759fa321abdb375affbe7 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Sat, 29 Jun 2024 16:38:44 -0400 Subject: [PATCH 009/106] bug fix --- benchmarks/MOABB/models/SpatialFocus.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/models/SpatialFocus.py b/benchmarks/MOABB/models/SpatialFocus.py index 94cb1f95a..0497e107e 100644 --- a/benchmarks/MOABB/models/SpatialFocus.py +++ b/benchmarks/MOABB/models/SpatialFocus.py @@ -57,7 +57,7 @@ def inject_spatial_focus( for idx, (name, mod) in enumerate(module.named_modules()): new_module.append(mod) if name == module_name: - new_module.append(mod) + new_module.append(spatial_focus) setattr(model, seq, new_module) return model From 0c6242f0ba198f296566325b7982977852aefef7 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Sat, 29 Jun 2024 16:54:48 -0400 Subject: [PATCH 010/106] explicitely define model --- .../EEGNet_with_spatial_focus.yaml | 9 +- .../MOABB/models/EEGNet_spatial_focus.py | 250 ++++++++++++++++++ benchmarks/MOABB/models/SpatialFocus.py | 16 -- benchmarks/MOABB/train.py | 3 +- 4 files changed, 253 insertions(+), 25 deletions(-) create mode 100644 benchmarks/MOABB/models/EEGNet_spatial_focus.py diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml index 7397a0408..69202c23b 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml @@ -155,7 +155,7 @@ dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" activation_type: "elu" -eeg_net: !new:models.EEGNet.EEGNet +model: !new:models.EEGNet_spatial_focus.EEGNet input_shape: !ref cnn_temporal_kernels: !ref cnn_temporal_kernelsize: [!ref , 1] @@ -171,10 +171,3 @@ eeg_net: !new:models.EEGNet.EEGNet dense_max_norm: !ref dropout: !ref dense_n_neurons: !ref -spatial_focus: !new:models.SpatialFocus.SpatialFocus - n_focal_points: !ref - focus_dims: 3 -model: !apply:models.SpatialFocus.inject_spatial_focus - model: !ref - spatial_focus: !ref - after: conv_module.bnorm_0 diff --git a/benchmarks/MOABB/models/EEGNet_spatial_focus.py b/benchmarks/MOABB/models/EEGNet_spatial_focus.py new file mode 100644 index 000000000..2a2f642ee --- /dev/null +++ b/benchmarks/MOABB/models/EEGNet_spatial_focus.py @@ -0,0 +1,250 @@ +"""EEGNet from https://doi.org/10.1088/1741-2552/aace8c. +Shallow and lightweight convolutional neural network proposed for a general decoding of single-trial EEG signals. +It was proposed for P300, error-related negativity, motor execution, motor imagery decoding. + +Authors + * Davide Borra, 2021 +""" +import torch +from models.SpatialFocus import SpatialFocus +import speechbrain as sb + + +class EEGNet(torch.nn.Module): + """EEGNet. + + Arguments + --------- + input_shape: tuple + The shape of the input. + cnn_temporal_kernels: int + Number of kernels in the 2d temporal convolution. + cnn_temporal_kernelsize: tuple + Kernel size of the 2d temporal convolution. + cnn_spatial_depth_multiplier: int + Depth multiplier of the 2d spatial depthwise convolution. + cnn_spatial_max_norm: float + Kernel max norm of the 2d spatial depthwise convolution. + cnn_spatial_pool: tuple + Pool size and stride after the 2d spatial depthwise convolution. + cnn_septemporal_depth_multiplier: int + Depth multiplier of the 2d temporal separable convolution. + cnn_septemporal_kernelsize: tuple + Kernel size of the 2d temporal separable convolution. + cnn_septemporal_pool: tuple + Pool size and stride after the 2d temporal separable convolution. + cnn_pool_type: string + Pooling type. + dropout: float + Dropout probability. + dense_max_norm: float + Weight max norm of the fully-connected layer. + dense_n_neurons: int + Number of output neurons. + activation_type: str + Activation function of the hidden layers. + + Example + ------- + #>>> inp_tensor = torch.rand([1, 200, 32, 1]) + #>>> model = EEGNet(input_shape=inp_tensor.shape) + #>>> output = model(inp_tensor) + #>>> output.shape + #torch.Size([1,4]) + """ + + def __init__( + self, + input_shape=None, # (1, T, C, 1) + cnn_temporal_kernels=8, + cnn_temporal_kernelsize=(33, 1), + cnn_spatial_depth_multiplier=2, + cnn_spatial_max_norm=1.0, + cnn_spatial_pool=(4, 1), + cnn_septemporal_depth_multiplier=1, + cnn_septemporal_point_kernels=None, + cnn_septemporal_kernelsize=(17, 1), + cnn_septemporal_pool=(8, 1), + cnn_pool_type="avg", + dropout=0.5, + dense_max_norm=0.25, + dense_n_neurons=4, + activation_type="elu", + ): + super().__init__() + if input_shape is None: + raise ValueError("Must specify input_shape") + if activation_type == "gelu": + activation = torch.nn.GELU() + elif activation_type == "elu": + activation = torch.nn.ELU() + elif activation_type == "relu": + activation = torch.nn.ReLU() + elif activation_type == "leaky_relu": + activation = torch.nn.LeakyReLU() + elif activation_type == "prelu": + activation = torch.nn.PReLU() + else: + raise ValueError("Wrong hidden activation function") + self.default_sf = 128 # sampling rate of the original publication (Hz) + # T = input_shape[1] + C = input_shape[2] + + # CONVOLUTIONAL MODULE + self.temporal_frontend = torch.nn.Sequential() + # Temporal convolution + self.temporal_frontend.add_module( + "conv_0", + sb.nnet.CNN.Conv2d( + in_channels=1, + out_channels=cnn_temporal_kernels, + kernel_size=cnn_temporal_kernelsize, + padding="same", + padding_mode="constant", + bias=False, + swap=True, + ), + ) + self.temporal_frontend.add_module( + "bnorm_0", + sb.nnet.normalization.BatchNorm2d( + input_size=cnn_temporal_kernels, momentum=0.01, affine=True, + ), + ) + self.spatial_focus = SpatialFocus(n_focal_points=C, focus_dims=3) + self.conv_module = torch.nn.Sequential() + # Spatial depthwise convolution + cnn_spatial_kernels = ( + cnn_spatial_depth_multiplier * cnn_temporal_kernels + ) + self.conv_module.add_module( + "conv_1", + sb.nnet.CNN.Conv2d( + in_channels=cnn_temporal_kernels, + out_channels=cnn_spatial_kernels, + kernel_size=(1, C), + groups=cnn_temporal_kernels, + padding="valid", + bias=False, + max_norm=cnn_spatial_max_norm, + swap=True, + ), + ) + self.conv_module.add_module( + "bnorm_1", + sb.nnet.normalization.BatchNorm2d( + input_size=cnn_spatial_kernels, momentum=0.01, affine=True, + ), + ) + self.conv_module.add_module("act_1", activation) + self.conv_module.add_module( + "pool_1", + sb.nnet.pooling.Pooling2d( + pool_type=cnn_pool_type, + kernel_size=cnn_spatial_pool, + stride=cnn_spatial_pool, + pool_axis=[1, 2], + ), + ) + self.conv_module.add_module("dropout_1", torch.nn.Dropout(p=dropout)) + + # Temporal separable convolution + cnn_septemporal_kernels = ( + cnn_spatial_kernels * cnn_septemporal_depth_multiplier + ) + self.conv_module.add_module( + "conv_2", + sb.nnet.CNN.Conv2d( + in_channels=cnn_spatial_kernels, + out_channels=cnn_septemporal_kernels, + kernel_size=cnn_septemporal_kernelsize, + groups=cnn_spatial_kernels, + padding="same", + padding_mode="constant", + bias=False, + swap=True, + ), + ) + + if cnn_septemporal_point_kernels is None: + cnn_septemporal_point_kernels = cnn_septemporal_kernels + + self.conv_module.add_module( + "conv_3", + sb.nnet.CNN.Conv2d( + in_channels=cnn_septemporal_kernels, + out_channels=cnn_septemporal_point_kernels, + kernel_size=(1, 1), + padding="valid", + bias=False, + swap=True, + ), + ) + self.conv_module.add_module( + "bnorm_3", + sb.nnet.normalization.BatchNorm2d( + input_size=cnn_septemporal_point_kernels, + momentum=0.01, + affine=True, + ), + ) + self.conv_module.add_module("act_3", activation) + self.conv_module.add_module( + "pool_3", + sb.nnet.pooling.Pooling2d( + pool_type=cnn_pool_type, + kernel_size=cnn_septemporal_pool, + stride=cnn_septemporal_pool, + pool_axis=[1, 2], + ), + ) + self.conv_module.add_module("dropout_3", torch.nn.Dropout(p=dropout)) + + # Shape of intermediate feature maps + out = self.conv_module( + torch.ones((1,) + tuple(input_shape[1:-1]) + (1,)) + ) + dense_input_size = self._num_flat_features(out) + # DENSE MODULE + self.dense_module = torch.nn.Sequential() + self.dense_module.add_module( + "flatten", torch.nn.Flatten(), + ) + self.dense_module.add_module( + "fc_out", + sb.nnet.linear.Linear( + input_size=dense_input_size, + n_neurons=dense_n_neurons, + max_norm=dense_max_norm, + ), + ) + self.dense_module.add_module("act_out", torch.nn.LogSoftmax(dim=1)) + + def _num_flat_features(self, x): + """Returns the number of flattened features from a tensor. + + Arguments + --------- + x : torch.Tensor + Input feature map. + """ + + size = x.size()[1:] # all dimensions except the batch dimension + num_features = 1 + for s in size: + num_features *= s + return num_features + + def forward(self, x, positions): + """Returns the output of the model. + + Arguments + --------- + x : torch.Tensor (batch, time, EEG channel, channel) + Input to convolve. 4d tensors are expected. + """ + x = self.temporal_frontend(x) + x = self.spatial_focus(x, positions) + x = self.conv_module(x) + x = self.dense_module(x) + return x diff --git a/benchmarks/MOABB/models/SpatialFocus.py b/benchmarks/MOABB/models/SpatialFocus.py index 0497e107e..c4c1d42f5 100644 --- a/benchmarks/MOABB/models/SpatialFocus.py +++ b/benchmarks/MOABB/models/SpatialFocus.py @@ -45,19 +45,3 @@ def forward(self, x: torch.Tensor, positions: torch.Tensor): x = (x * weights).sum(dim=-2) return x - - -def inject_spatial_focus( - model: nn.Module, spatial_focus: SpatialFocus, after: str -): - seq, module_name = after.split(".") - - module: nn.Sequential = getattr(model, seq) - new_module = nn.Sequential() - for idx, (name, mod) in enumerate(module.named_modules()): - new_module.append(mod) - if name == module_name: - new_module.append(spatial_focus) - - setattr(model, seq, new_module) - return model diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 78d3fa058..f238b7685 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -54,7 +54,7 @@ def compute_forward(self, batch, stage): # Normalization if hasattr(self.hparams, "normalize"): inputs = self.hparams.normalize(inputs) - return self.modules.model(inputs) + return self.modules.model(inputs, self.hparams.ch_positions) def compute_objectives(self, predictions, batch, stage): "Given the network predictions and targets computes the loss." @@ -285,6 +285,7 @@ def run_experiment(hparams, run_opts, datasets): datasets["test"].dataset.tensors[0].shape[0], ) logger.info(datasets_summary) + hparams["ch_positions"] = datasets["ch_positions"] brain = MOABBBrain( modules={"model": hparams["model"]}, From 99647decd3c4f4b6ec1f6e58742a91fa10b3b6b1 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Sat, 29 Jun 2024 16:58:40 -0400 Subject: [PATCH 011/106] update softmax dim --- benchmarks/MOABB/models/SpatialFocus.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/models/SpatialFocus.py b/benchmarks/MOABB/models/SpatialFocus.py index c4c1d42f5..9934a73ae 100644 --- a/benchmarks/MOABB/models/SpatialFocus.py +++ b/benchmarks/MOABB/models/SpatialFocus.py @@ -8,7 +8,7 @@ def __init__( n_focal_points, focus_dims=3, similarity_func="cosine", - similarity_transform=nn.Softmax(-1), + similarity_transform=nn.Softmax(-2), ): super().__init__() self.n_focal_points = n_focal_points From 0c7e0d1233865be8cd74e2e888cd3ea39232a602 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Sat, 29 Jun 2024 16:59:21 -0400 Subject: [PATCH 012/106] update sum dim --- benchmarks/MOABB/models/SpatialFocus.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/models/SpatialFocus.py b/benchmarks/MOABB/models/SpatialFocus.py index 9934a73ae..452a477e0 100644 --- a/benchmarks/MOABB/models/SpatialFocus.py +++ b/benchmarks/MOABB/models/SpatialFocus.py @@ -42,6 +42,6 @@ def forward(self, x: torch.Tensor, positions: torch.Tensor): weights = weights.unsqueeze(-1) # Feature dimension x = x.unsqueeze(-2) # Focus dimension - x = (x * weights).sum(dim=-2) + x = (x * weights).sum(dim=-3) return x From 65197322075501ab1e305876f9fa560f7a5496d1 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 13:23:43 -0400 Subject: [PATCH 013/106] fix num flat features --- benchmarks/MOABB/models/EEGNet_spatial_focus.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/benchmarks/MOABB/models/EEGNet_spatial_focus.py b/benchmarks/MOABB/models/EEGNet_spatial_focus.py index 2a2f642ee..2ea4b3c73 100644 --- a/benchmarks/MOABB/models/EEGNet_spatial_focus.py +++ b/benchmarks/MOABB/models/EEGNet_spatial_focus.py @@ -201,10 +201,7 @@ def __init__( self.conv_module.add_module("dropout_3", torch.nn.Dropout(p=dropout)) # Shape of intermediate feature maps - out = self.conv_module( - torch.ones((1,) + tuple(input_shape[1:-1]) + (1,)) - ) - dense_input_size = self._num_flat_features(out) + dense_input_size = self._num_flat_features(input_shape) # DENSE MODULE self.dense_module = torch.nn.Sequential() self.dense_module.add_module( @@ -220,7 +217,7 @@ def __init__( ) self.dense_module.add_module("act_out", torch.nn.LogSoftmax(dim=1)) - def _num_flat_features(self, x): + def _num_flat_features(self, input_shape): """Returns the number of flattened features from a tensor. Arguments @@ -228,7 +225,11 @@ def _num_flat_features(self, x): x : torch.Tensor Input feature map. """ - + x = self.temporal_frontend( + torch.ones((1,) + tuple(input_shape[1:-1]) + (1,)) + ) + x = self.spatial_focus(x) + x = self.conv_module(x) size = x.size()[1:] # all dimensions except the batch dimension num_features = 1 for s in size: From 5cb5840e72a21b99013b8c692cda7a752d9e477f Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 13:24:37 -0400 Subject: [PATCH 014/106] don't apply spatial focus during num flat features (because the shape remains unchanged) --- benchmarks/MOABB/models/EEGNet_spatial_focus.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/models/EEGNet_spatial_focus.py b/benchmarks/MOABB/models/EEGNet_spatial_focus.py index 2ea4b3c73..e1ede942b 100644 --- a/benchmarks/MOABB/models/EEGNet_spatial_focus.py +++ b/benchmarks/MOABB/models/EEGNet_spatial_focus.py @@ -228,7 +228,7 @@ def _num_flat_features(self, input_shape): x = self.temporal_frontend( torch.ones((1,) + tuple(input_shape[1:-1]) + (1,)) ) - x = self.spatial_focus(x) + # x = self.spatial_focus(x) x = self.conv_module(x) size = x.size()[1:] # all dimensions except the batch dimension num_features = 1 From c56b6fdbea4610ac79fb6170487998e4b1e4fe32 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 13:26:19 -0400 Subject: [PATCH 015/106] update dataio iterators --- benchmarks/MOABB/utils/dataio_iterators.py | 153 +++++++++++++++++---- 1 file changed, 130 insertions(+), 23 deletions(-) diff --git a/benchmarks/MOABB/utils/dataio_iterators.py b/benchmarks/MOABB/utils/dataio_iterators.py index 83552c396..bec4ec929 100644 --- a/benchmarks/MOABB/utils/dataio_iterators.py +++ b/benchmarks/MOABB/utils/dataio_iterators.py @@ -15,13 +15,15 @@ import abc import os from collections import namedtuple -from typing import Dict, List, Optional, Tuple, TypedDict +from typing import Dict, List, Optional, Protocol, Tuple, TypedDict import numpy as np import pandas as pd import torch from moabb.datasets.base import BaseDataset as MOABBDataset from torch.utils.data import DataLoader, TensorDataset +import torch_geometric.data +import torch_geometric.loader from utils.prepare import prepare_data @@ -135,6 +137,12 @@ def sample_channels(x, adjacency_mtx, ch_names, n_steps, seed_nodes=["Cz"]): return x, idx_sel_channels, ch_names +def adjacency_mtx_to_edge_list(adjacency_mtx): + """Convert adjacency matrix to edge list format""" + edge_list = np.vstack(np.nonzero(adjacency_mtx)) + return torch.tensor(edge_list, dtype=torch.long).contiguous() + + class _SplitDataloaders(TypedDict): train: DataLoader valid: DataLoader @@ -158,7 +166,31 @@ class _DataDict(TypedDict): subject: str -class BaseDataIOIterator(abc.ABC): +class DataLoaderFactory(Protocol): + def prepare( + self, + *, + data_folder: str, + cached_data_folder: str, + dataset: MOABBDataset, + batch_size: int, + valid_ratio: float, + original_sample_rate: int, + target_subject_idx: int, + target_session_idx: Optional[int] = None, + sample_rate: Optional[int] = None, + fmin: Optional[float] = None, + fmax: Optional[float] = None, + tmin: Optional[float] = None, + tmax: Optional[float] = None, + n_steps_channel_selection: Optional[int] = None, + events_to_load: Optional[List[str]] = None, + save_prepared_dataset: bool = True, + ) -> Tuple[str, _SplitDataloaders]: + ... + + +class BaseDataIOIterator(DataLoaderFactory, abc.ABC): def __init__(self, tag, seed): self.iterator_tag = tag np.random.seed(seed) @@ -239,8 +271,6 @@ def prepare( interval = [tmin, tmax] subject = dataset.subject_list[target_subject_idx] - self.validate_dataset_or_raise(dataset) - ( target_data_dict, (x_train, y_train, x_valid, y_valid, x_test, y_test), @@ -280,26 +310,34 @@ def prepare( interval_out=interval, ) + adjacency_mtx = target_data_dict["adjacency_mtx"] + ch_positions = target_data_dict["ch_positions"] + ch_names = target_data_dict["channels"] + # channel sampling if n_steps_channel_selection is not None: - x_train, ch_idx, ch_names = sample_channels( + x_train, ch_idx, ch_names_sel = sample_channels( x_train, - target_data_dict["adjacency_mtx"], - target_data_dict["channels"], + adjacency_mtx, + ch_names, n_steps=n_steps_channel_selection, ) x_valid, *_ = sample_channels( x_valid, - target_data_dict["adjacency_mtx"], - target_data_dict["channels"], + adjacency_mtx, + ch_names, n_steps=n_steps_channel_selection, ) x_test, *_ = sample_channels( x_test, - target_data_dict["adjacency_mtx"], - target_data_dict["channels"], + adjacency_mtx, + ch_names, n_steps=n_steps_channel_selection, ) + adjacency_mtx = adjacency_mtx[ch_idx, :][:, ch_idx] + ch_names = ch_names_sel + + ch_positions = np.row_stack([ch_positions[ch] for ch in ch_names]) # swap axes: from (N_examples, C, T) to (N_examples, T, C) x_train = np.swapaxes(x_train, -1, -2) @@ -311,27 +349,18 @@ def prepare( batch_size, (x_train, y_train), (x_valid, y_valid), (x_test, y_test) ) - ch_positions = target_data_dict["ch_positions"] - ch_positions = np.row_stack([ch_positions[ch] for ch in ch_names]) - - adj_mtx = target_data_dict["adjacency_mtx"][ch_idx, :][:, ch_idx] - datasets: _SplitDataloaders = dict( train=train_loader, valid=valid_loader, test=test_loader, ch_names=ch_names, - channel_positions=ch_positions, - adjacency_mtx=adj_mtx, + ch_positions=ch_positions, + adjacency_mtx=adjacency_mtx, ) tail_path = self.get_tail_path(subject, target_data_dict.get("session")) return tail_path, datasets - def validate_dataset_or_raise(self, dataset: MOABBDataset) -> None: - """Check that the dataset is compatible, or raise an error.""" - pass - @abc.abstractmethod def get_data( self, @@ -374,6 +403,83 @@ def get_tail_path(self, subject: int, session: Optional[str]) -> str: return tail_path +class AsGraph(DataLoaderFactory): + def __init__(self, inner: DataLoaderFactory): + self.inner = inner + + def prepare( + self, + *, + data_folder: str, + cached_data_folder: str, + dataset: MOABBDataset, + batch_size: int, + valid_ratio: float, + original_sample_rate: int, + target_subject_idx: int, + target_session_idx: Optional[int] = None, + sample_rate: Optional[int] = None, + fmin: Optional[float] = None, + fmax: Optional[float] = None, + tmin: Optional[float] = None, + tmax: Optional[float] = None, + n_steps_channel_selection: Optional[int] = None, + events_to_load: Optional[List[str]] = None, + save_prepared_dataset: bool = True, + ) -> Tuple[str, _SplitDataloaders]: + tail_path, datasets = self.inner.prepare( + data_folder=data_folder, + cached_data_folder=cached_data_folder, + dataset=dataset, + batch_size=batch_size, + valid_ratio=valid_ratio, + original_sample_rate=original_sample_rate, + target_subject_idx=target_subject_idx, + target_session_idx=target_session_idx, + sample_rate=sample_rate, + fmin=fmin, + fmax=fmax, + tmin=tmin, + tmax=tmax, + n_steps_channel_selection=n_steps_channel_selection, + events_to_load=events_to_load, + save_prepared_dataset=save_prepared_dataset, + ) + + edge_list = adjacency_mtx_to_edge_list(datasets["adjacency_mtx"]) + + for key in ("train", "valid", "test"): + datasets[key] = self.make_graph_dataloader( + datasets[key], + edge_list=edge_list, + ch_positions=torch.from_numpy(datasets["ch_positions"]), + shuffle=(key == "train"), + ) + + return tail_path, datasets + + def make_graph_dataloader( + self, + dataloader: DataLoader, + edge_list: torch.Tensor, + ch_positions: torch.Tensor, + shuffle: bool = False, + ) -> torch_geometric.loader.DataLoader: + dataset = [ + # Transpose T, C -> C, T + torch_geometric.data.Data( + x=x.transpose(0, 1), y=y, edge_index=edge_list, pos=ch_positions + ) + for x, y in dataloader.dataset + ] + return torch_geometric.loader.DataLoader( + dataset, + batch_size=dataloader.batch_size, + pin_memory=dataloader.pin_memory, + shuffle=shuffle, + ) + + class LeaveOneSessionOut(BaseDataIOIterator): """Leave one session out iterator for MOABB datasets. Designing within-subject, cross-session and session-agnostic iterations on the dataset for a specific paradigm. @@ -505,6 +611,7 @@ def get_data( valid_ratio=None, **prepare_data_kwargs, ): + self._validate_dataset_or_raise(dataset) # preparing or loading test set target_data_dict = prepare_data( dataset=dataset, @@ -584,7 +691,7 @@ def get_data( (x_train, y_train, x_valid, y_valid, x_test, y_test,), ) - def validate_dataset_or_raise(self, dataset: MOABBDataset): + def _validate_dataset_or_raise(self, dataset: MOABBDataset): if len(dataset.subject_list) < 2: raise ( ValueError( From 24eae4ac1c1896432c71b782e85330b92825f2fc Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 13:27:34 -0400 Subject: [PATCH 016/106] add torch_geometric to requirements --- benchmarks/MOABB/extra-requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/benchmarks/MOABB/extra-requirements.txt b/benchmarks/MOABB/extra-requirements.txt index 21f8c3838..2d03719b8 100644 --- a/benchmarks/MOABB/extra-requirements.txt +++ b/benchmarks/MOABB/extra-requirements.txt @@ -3,4 +3,5 @@ moabb orion orion[profet] scikit-learn +torch_geometric torchinfo From 596d7c699234be505140f2ccd197ffa83a330839 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 13:31:15 -0400 Subject: [PATCH 017/106] default positions to support torchsummary --- benchmarks/MOABB/models/EEGNet_spatial_focus.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/benchmarks/MOABB/models/EEGNet_spatial_focus.py b/benchmarks/MOABB/models/EEGNet_spatial_focus.py index e1ede942b..5636b14ae 100644 --- a/benchmarks/MOABB/models/EEGNet_spatial_focus.py +++ b/benchmarks/MOABB/models/EEGNet_spatial_focus.py @@ -236,7 +236,7 @@ def _num_flat_features(self, input_shape): num_features *= s return num_features - def forward(self, x, positions): + def forward(self, x, positions=None): """Returns the output of the model. Arguments @@ -244,6 +244,8 @@ def forward(self, x, positions): x : torch.Tensor (batch, time, EEG channel, channel) Input to convolve. 4d tensors are expected. """ + if positions is None: + positions = torch.zeros((x.shape[-2], 3), device=x.device) x = self.temporal_frontend(x) x = self.spatial_focus(x, positions) x = self.conv_module(x) From 873527e4f667ecd1cb7ec135163e8ac469756554 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 13:34:36 -0400 Subject: [PATCH 018/106] move positions to device --- benchmarks/MOABB/train.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index f238b7685..0c236db82 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -83,6 +83,7 @@ def compute_objectives(self, predictions, batch, stage): def on_fit_start(self,): """Gets called at the beginning of ``fit()``""" + self.hparams.ch_positions = self.hparams.ch_positions.to(self.device) self.init_model(self.hparams.model) self.init_optimizers() in_shape = ( @@ -285,7 +286,7 @@ def run_experiment(hparams, run_opts, datasets): datasets["test"].dataset.tensors[0].shape[0], ) logger.info(datasets_summary) - hparams["ch_positions"] = datasets["ch_positions"] + hparams["ch_positions"] = torch.from_numpy(datasets["ch_positions"]) brain = MOABBBrain( modules={"model": hparams["model"]}, From 1baf576096631154372a6d5621e5cc24a855dff9 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 13:35:44 -0400 Subject: [PATCH 019/106] convert positions to float --- benchmarks/MOABB/train.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 0c236db82..04dc5c837 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -83,7 +83,9 @@ def compute_objectives(self, predictions, batch, stage): def on_fit_start(self,): """Gets called at the beginning of ``fit()``""" - self.hparams.ch_positions = self.hparams.ch_positions.to(self.device) + self.hparams.ch_positions = self.hparams.ch_positions.float().to( + self.device + ) self.init_model(self.hparams.model) self.init_optimizers() in_shape = ( From f59e21f52149a16d6b9eebe6bd4fd859bc3d9a19 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 13:47:42 -0400 Subject: [PATCH 020/106] update hparams for spatial focus --- .../MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml index 69202c23b..275987daa 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml @@ -34,8 +34,7 @@ n_classes: 4 # -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) tmin: 0. tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" -# number of steps used when selecting adjacent channels from a seed channel (default at Cz) -n_steps_channel_selection: 2 # @orion_step1: --n_steps_channel_selection~"uniform(1, 3,discrete=True)" +n_steps_channel_selection: null T: !apply:math.ceil - !ref * ( - ) C: 22 @@ -130,7 +129,7 @@ dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) normalize: !name:speechbrain.processing.signal_processing.mean_std_norm dims: !ref # MODEL -number_of_foci: 22 # @orion_step1: --number_of_foci~"uniform(4, 64, discrete=True)" +number_of_foci: 22 # @orion_step1: --number_of_foci~"uniform(4, 44, discrete=True)" input_shape: [null, !ref , !ref , null] cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(4, 64,discrete=True)" cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" From f40a6ea83ce0b3a5d0c09aac13eb003d5fd46d84 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 14:09:44 -0400 Subject: [PATCH 021/106] init train_slurm script --- benchmarks/MOABB/scripts/train_slurm.sh | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 benchmarks/MOABB/scripts/train_slurm.sh diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/train_slurm.sh new file mode 100644 index 000000000..f800102a9 --- /dev/null +++ b/benchmarks/MOABB/scripts/train_slurm.sh @@ -0,0 +1,11 @@ +module load StdEnv/2020 +module load python/3.10.2 +module load scipy-stack + +virtualenv $SLURM_TMPDIR/venv +source $SLURM_TMPDIR/venv/bin/activate + +pip install --no-index $SCRATCH/wheels/* + +cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB +pip install --no-index -r extra_requirements.txt From 7efd77d28af4d78bedcc4f221506748d11c76419 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 14:14:59 -0400 Subject: [PATCH 022/106] venv no download --- benchmarks/MOABB/scripts/train_slurm.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/train_slurm.sh index f800102a9..318a25738 100644 --- a/benchmarks/MOABB/scripts/train_slurm.sh +++ b/benchmarks/MOABB/scripts/train_slurm.sh @@ -2,7 +2,7 @@ module load StdEnv/2020 module load python/3.10.2 module load scipy-stack -virtualenv $SLURM_TMPDIR/venv +virtualenv --no-download $SLURM_TMPDIR/venv source $SLURM_TMPDIR/venv/bin/activate pip install --no-index $SCRATCH/wheels/* From d110e2b89d4b623dbc701bdfca79f80fcbcbca06 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 14:15:17 -0400 Subject: [PATCH 023/106] chmod --- benchmarks/MOABB/scripts/train_slurm.sh | 0 1 file changed, 0 insertions(+), 0 deletions(-) mode change 100644 => 100755 benchmarks/MOABB/scripts/train_slurm.sh diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/train_slurm.sh old mode 100644 new mode 100755 From eec4fe7c014ad36afa46598dd357a3802d21e845 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 14:17:04 -0400 Subject: [PATCH 024/106] typo --- benchmarks/MOABB/scripts/train_slurm.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/train_slurm.sh index 318a25738..4a5b6e6ea 100755 --- a/benchmarks/MOABB/scripts/train_slurm.sh +++ b/benchmarks/MOABB/scripts/train_slurm.sh @@ -8,4 +8,4 @@ source $SLURM_TMPDIR/venv/bin/activate pip install --no-index $SCRATCH/wheels/* cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB -pip install --no-index -r extra_requirements.txt +pip install --no-index -r extra-requirements.txt From ff0f0cc406dfcc2f2da7623cf5f4a20a024b6c03 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 14:17:52 -0400 Subject: [PATCH 025/106] remove mne constraint --- benchmarks/MOABB/extra-requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/extra-requirements.txt b/benchmarks/MOABB/extra-requirements.txt index 2d03719b8..90e861602 100644 --- a/benchmarks/MOABB/extra-requirements.txt +++ b/benchmarks/MOABB/extra-requirements.txt @@ -1,4 +1,4 @@ -mne==1.6.1 +mne moabb orion orion[profet] From 26e1de182df973f3d4dfdc48884970fa9041b00c Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 14:28:17 -0400 Subject: [PATCH 026/106] install h5py manually --- benchmarks/MOABB/scripts/train_slurm.sh | 1 + 1 file changed, 1 insertion(+) diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/train_slurm.sh index 4a5b6e6ea..454e3c511 100755 --- a/benchmarks/MOABB/scripts/train_slurm.sh +++ b/benchmarks/MOABB/scripts/train_slurm.sh @@ -6,6 +6,7 @@ virtualenv --no-download $SLURM_TMPDIR/venv source $SLURM_TMPDIR/venv/bin/activate pip install --no-index $SCRATCH/wheels/* +pip install --no-index h5py cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB pip install --no-index -r extra-requirements.txt From ed1ca3a3eeeef9f0e2ce25207bec2dba5ce6e7bc Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 14:41:21 -0400 Subject: [PATCH 027/106] don't install from extra_requiremnts --- benchmarks/MOABB/scripts/train_slurm.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/train_slurm.sh index 454e3c511..8803d05d4 100755 --- a/benchmarks/MOABB/scripts/train_slurm.sh +++ b/benchmarks/MOABB/scripts/train_slurm.sh @@ -6,7 +6,7 @@ virtualenv --no-download $SLURM_TMPDIR/venv source $SLURM_TMPDIR/venv/bin/activate pip install --no-index $SCRATCH/wheels/* -pip install --no-index h5py +pip install --no-index h5py orion scikit-learn torch_geometric torchinfo + cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB -pip install --no-index -r extra-requirements.txt From 1888bfa94a92fa4d1832d5751b1bf978b85b3b0f Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 14:51:10 -0400 Subject: [PATCH 028/106] update script --- benchmarks/MOABB/scripts/train_slurm.sh | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/train_slurm.sh index 8803d05d4..06c11fef1 100755 --- a/benchmarks/MOABB/scripts/train_slurm.sh +++ b/benchmarks/MOABB/scripts/train_slurm.sh @@ -5,8 +5,15 @@ module load scipy-stack virtualenv --no-download $SLURM_TMPDIR/venv source $SLURM_TMPDIR/venv/bin/activate -pip install --no-index $SCRATCH/wheels/* -pip install --no-index h5py orion scikit-learn torch_geometric torchinfo +pip install --no-index "h5py<4.0.0" scikit-learn orion torch_geometric torchinfo +pip install --no-index $SCRATCH/wheels/EDFlib_Python-1.0.8-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/edfio-0.4.3-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/mne-1.7.1-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/mne_bids-0.15.0-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/pymatreader-0.0.32-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/pyriemann-0.6-py2.py3-none-any.whl +pip install --no-index $SCRATCH/wheels/speechbrain-1.0.0-py3-none-any.whl +pip install --no-index --no-deps $SCRATCH/wheels/moabb-1.1.0-py3-none-any.whl cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB From b44f721be91e58301c913ff9529efd3165276f8a Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 15:04:08 -0400 Subject: [PATCH 029/106] fix h5py requirement --- benchmarks/MOABB/scripts/train_slurm.sh | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/train_slurm.sh index 06c11fef1..ee1d79183 100755 --- a/benchmarks/MOABB/scripts/train_slurm.sh +++ b/benchmarks/MOABB/scripts/train_slurm.sh @@ -5,7 +5,7 @@ module load scipy-stack virtualenv --no-download $SLURM_TMPDIR/venv source $SLURM_TMPDIR/venv/bin/activate -pip install --no-index "h5py<4.0.0" scikit-learn orion torch_geometric torchinfo +pip install --no-index scikit-learn orion torch_geometric torchinfo pip install --no-index $SCRATCH/wheels/EDFlib_Python-1.0.8-py3-none-any.whl pip install --no-index $SCRATCH/wheels/edfio-0.4.3-py3-none-any.whl pip install --no-index $SCRATCH/wheels/mne-1.7.1-py3-none-any.whl @@ -13,7 +13,8 @@ pip install --no-index $SCRATCH/wheels/mne_bids-0.15.0-py3-none-any.whl pip install --no-index $SCRATCH/wheels/pymatreader-0.0.32-py3-none-any.whl pip install --no-index $SCRATCH/wheels/pyriemann-0.6-py2.py3-none-any.whl pip install --no-index $SCRATCH/wheels/speechbrain-1.0.0-py3-none-any.whl -pip install --no-index --no-deps $SCRATCH/wheels/moabb-1.1.0-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/h5py-3.11.0.tar.gz +pip install --no-index $SCRATCH/wheels/moabb-1.1.0-py3-none-any.whl cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB From a3b348fe2a37372029e190ea5885ee1f988a7554 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 15:09:42 -0400 Subject: [PATCH 030/106] move h5py higher --- benchmarks/MOABB/scripts/train_slurm.sh | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/train_slurm.sh index ee1d79183..aee524a46 100755 --- a/benchmarks/MOABB/scripts/train_slurm.sh +++ b/benchmarks/MOABB/scripts/train_slurm.sh @@ -5,7 +5,9 @@ module load scipy-stack virtualenv --no-download $SLURM_TMPDIR/venv source $SLURM_TMPDIR/venv/bin/activate +# NOTE: The order of installs is important! pip install --no-index scikit-learn orion torch_geometric torchinfo +pip install --no-index $SCRATCH/wheels/h5py-3.11.0.tar.gz pip install --no-index $SCRATCH/wheels/EDFlib_Python-1.0.8-py3-none-any.whl pip install --no-index $SCRATCH/wheels/edfio-0.4.3-py3-none-any.whl pip install --no-index $SCRATCH/wheels/mne-1.7.1-py3-none-any.whl @@ -13,7 +15,6 @@ pip install --no-index $SCRATCH/wheels/mne_bids-0.15.0-py3-none-any.whl pip install --no-index $SCRATCH/wheels/pymatreader-0.0.32-py3-none-any.whl pip install --no-index $SCRATCH/wheels/pyriemann-0.6-py2.py3-none-any.whl pip install --no-index $SCRATCH/wheels/speechbrain-1.0.0-py3-none-any.whl -pip install --no-index $SCRATCH/wheels/h5py-3.11.0.tar.gz pip install --no-index $SCRATCH/wheels/moabb-1.1.0-py3-none-any.whl From 5155895352da03f01c379e097381478fd60378c3 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 15:18:50 -0400 Subject: [PATCH 031/106] update moabb version --- benchmarks/MOABB/scripts/train_slurm.sh | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/train_slurm.sh index aee524a46..bae5880ee 100755 --- a/benchmarks/MOABB/scripts/train_slurm.sh +++ b/benchmarks/MOABB/scripts/train_slurm.sh @@ -7,7 +7,6 @@ source $SLURM_TMPDIR/venv/bin/activate # NOTE: The order of installs is important! pip install --no-index scikit-learn orion torch_geometric torchinfo -pip install --no-index $SCRATCH/wheels/h5py-3.11.0.tar.gz pip install --no-index $SCRATCH/wheels/EDFlib_Python-1.0.8-py3-none-any.whl pip install --no-index $SCRATCH/wheels/edfio-0.4.3-py3-none-any.whl pip install --no-index $SCRATCH/wheels/mne-1.7.1-py3-none-any.whl @@ -15,7 +14,7 @@ pip install --no-index $SCRATCH/wheels/mne_bids-0.15.0-py3-none-any.whl pip install --no-index $SCRATCH/wheels/pymatreader-0.0.32-py3-none-any.whl pip install --no-index $SCRATCH/wheels/pyriemann-0.6-py2.py3-none-any.whl pip install --no-index $SCRATCH/wheels/speechbrain-1.0.0-py3-none-any.whl -pip install --no-index $SCRATCH/wheels/moabb-1.1.0-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/moabb-1.0.0-py3-none-any.whl cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB From 05ebec79051b2e1655dae8cc3a2f9ee8d0b9bc3c Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 15:19:31 -0400 Subject: [PATCH 032/106] update mne version --- benchmarks/MOABB/scripts/train_slurm.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/train_slurm.sh index bae5880ee..3ce9f1d77 100755 --- a/benchmarks/MOABB/scripts/train_slurm.sh +++ b/benchmarks/MOABB/scripts/train_slurm.sh @@ -9,7 +9,7 @@ source $SLURM_TMPDIR/venv/bin/activate pip install --no-index scikit-learn orion torch_geometric torchinfo pip install --no-index $SCRATCH/wheels/EDFlib_Python-1.0.8-py3-none-any.whl pip install --no-index $SCRATCH/wheels/edfio-0.4.3-py3-none-any.whl -pip install --no-index $SCRATCH/wheels/mne-1.7.1-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/mne-1.6.1-py3-none-any.whl pip install --no-index $SCRATCH/wheels/mne_bids-0.15.0-py3-none-any.whl pip install --no-index $SCRATCH/wheels/pymatreader-0.0.32-py3-none-any.whl pip install --no-index $SCRATCH/wheels/pyriemann-0.6-py2.py3-none-any.whl From 3948676f839f30229725aee9aa7fe52fe5d5dd80 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 15:22:32 -0400 Subject: [PATCH 033/106] memory profiler req --- benchmarks/MOABB/scripts/train_slurm.sh | 1 + 1 file changed, 1 insertion(+) diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/train_slurm.sh index 3ce9f1d77..6595530db 100755 --- a/benchmarks/MOABB/scripts/train_slurm.sh +++ b/benchmarks/MOABB/scripts/train_slurm.sh @@ -14,6 +14,7 @@ pip install --no-index $SCRATCH/wheels/mne_bids-0.15.0-py3-none-any.whl pip install --no-index $SCRATCH/wheels/pymatreader-0.0.32-py3-none-any.whl pip install --no-index $SCRATCH/wheels/pyriemann-0.6-py2.py3-none-any.whl pip install --no-index $SCRATCH/wheels/speechbrain-1.0.0-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/memory_profiler-0.61.0-py3-none-any.whl pip install --no-index $SCRATCH/wheels/moabb-1.0.0-py3-none-any.whl From a31dc4eb6c897734030eb79365a0342a6960aa48 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 15:26:37 -0400 Subject: [PATCH 034/106] update mne bids version --- benchmarks/MOABB/scripts/train_slurm.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/train_slurm.sh index 6595530db..09df87ce8 100755 --- a/benchmarks/MOABB/scripts/train_slurm.sh +++ b/benchmarks/MOABB/scripts/train_slurm.sh @@ -10,7 +10,7 @@ pip install --no-index scikit-learn orion torch_geometric torchinfo pip install --no-index $SCRATCH/wheels/EDFlib_Python-1.0.8-py3-none-any.whl pip install --no-index $SCRATCH/wheels/edfio-0.4.3-py3-none-any.whl pip install --no-index $SCRATCH/wheels/mne-1.6.1-py3-none-any.whl -pip install --no-index $SCRATCH/wheels/mne_bids-0.15.0-py3-none-any.whl +pip install --no-index $SCRATCH/wheels/mne_bids-0.13-py2.py3-none-any.whl pip install --no-index $SCRATCH/wheels/pymatreader-0.0.32-py3-none-any.whl pip install --no-index $SCRATCH/wheels/pyriemann-0.6-py2.py3-none-any.whl pip install --no-index $SCRATCH/wheels/speechbrain-1.0.0-py3-none-any.whl From 1aecf5fb14af5e37bbe0619991c5964d27e9539c Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 15:34:11 -0400 Subject: [PATCH 035/106] update pyriemann version --- .../MOABB/scripts/{train_slurm.sh => slurm_install_env.sh} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename benchmarks/MOABB/scripts/{train_slurm.sh => slurm_install_env.sh} (93%) diff --git a/benchmarks/MOABB/scripts/train_slurm.sh b/benchmarks/MOABB/scripts/slurm_install_env.sh similarity index 93% rename from benchmarks/MOABB/scripts/train_slurm.sh rename to benchmarks/MOABB/scripts/slurm_install_env.sh index 09df87ce8..418a1adf3 100755 --- a/benchmarks/MOABB/scripts/train_slurm.sh +++ b/benchmarks/MOABB/scripts/slurm_install_env.sh @@ -12,7 +12,7 @@ pip install --no-index $SCRATCH/wheels/edfio-0.4.3-py3-none-any.whl pip install --no-index $SCRATCH/wheels/mne-1.6.1-py3-none-any.whl pip install --no-index $SCRATCH/wheels/mne_bids-0.13-py2.py3-none-any.whl pip install --no-index $SCRATCH/wheels/pymatreader-0.0.32-py3-none-any.whl -pip install --no-index $SCRATCH/wheels/pyriemann-0.6-py2.py3-none-any.whl +pip install --no-index $SCRATCH/wheels/pyriemann-0.5-py2.py3-none-any.whl pip install --no-index $SCRATCH/wheels/speechbrain-1.0.0-py3-none-any.whl pip install --no-index $SCRATCH/wheels/memory_profiler-0.61.0-py3-none-any.whl pip install --no-index $SCRATCH/wheels/moabb-1.0.0-py3-none-any.whl From cb0368fb3c2ec29126a1162e78c789c6c44352d1 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 15:51:47 -0400 Subject: [PATCH 036/106] update scripts --- benchmarks/MOABB/scripts/slurm_install_env.sh | 3 --- benchmarks/MOABB/scripts/slurm_train_base.sh | 6 ++++++ 2 files changed, 6 insertions(+), 3 deletions(-) create mode 100644 benchmarks/MOABB/scripts/slurm_train_base.sh diff --git a/benchmarks/MOABB/scripts/slurm_install_env.sh b/benchmarks/MOABB/scripts/slurm_install_env.sh index 418a1adf3..237f2fe37 100755 --- a/benchmarks/MOABB/scripts/slurm_install_env.sh +++ b/benchmarks/MOABB/scripts/slurm_install_env.sh @@ -16,6 +16,3 @@ pip install --no-index $SCRATCH/wheels/pyriemann-0.5-py2.py3-none-any.whl pip install --no-index $SCRATCH/wheels/speechbrain-1.0.0-py3-none-any.whl pip install --no-index $SCRATCH/wheels/memory_profiler-0.61.0-py3-none-any.whl pip install --no-index $SCRATCH/wheels/moabb-1.0.0-py3-none-any.whl - - -cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB diff --git a/benchmarks/MOABB/scripts/slurm_train_base.sh b/benchmarks/MOABB/scripts/slurm_train_base.sh new file mode 100644 index 000000000..64fc7c7c0 --- /dev/null +++ b/benchmarks/MOABB/scripts/slurm_train_base.sh @@ -0,0 +1,6 @@ +source $HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_install_env.sh + +mkdir -p $SLURM_TMPDIR/mne_data +tar -axf $HOME/projects/def-ravanelm/datasets-open/${dataset_tag}.tar -C $SLURM_TMPDIR/mne_data + +python train.py $@ --data_folder=$SLURM_TMPDIR/mne_data --cached_data_folder=$SLURM_TMPDIR/mne_data/pkl From 5537299a7ec514ee345959d6c6d682d6fa034d0d Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 15:57:21 -0400 Subject: [PATCH 037/106] fix cd --- benchmarks/MOABB/scripts/slurm_train_base.sh | 1 + 1 file changed, 1 insertion(+) diff --git a/benchmarks/MOABB/scripts/slurm_train_base.sh b/benchmarks/MOABB/scripts/slurm_train_base.sh index 64fc7c7c0..382e3f267 100644 --- a/benchmarks/MOABB/scripts/slurm_train_base.sh +++ b/benchmarks/MOABB/scripts/slurm_train_base.sh @@ -3,4 +3,5 @@ source $HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_insta mkdir -p $SLURM_TMPDIR/mne_data tar -axf $HOME/projects/def-ravanelm/datasets-open/${dataset_tag}.tar -C $SLURM_TMPDIR/mne_data +cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB python train.py $@ --data_folder=$SLURM_TMPDIR/mne_data --cached_data_folder=$SLURM_TMPDIR/mne_data/pkl From d7ba569bf196aace010ebe6dfcafd0ab5a4e46ba Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 16:17:02 -0400 Subject: [PATCH 038/106] normalize positions --- benchmarks/MOABB/scripts/slurm_train_base.sh | 0 benchmarks/MOABB/train.py | 4 ++++ 2 files changed, 4 insertions(+) mode change 100644 => 100755 benchmarks/MOABB/scripts/slurm_train_base.sh diff --git a/benchmarks/MOABB/scripts/slurm_train_base.sh b/benchmarks/MOABB/scripts/slurm_train_base.sh old mode 100644 new mode 100755 diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 04dc5c837..71c26f63b 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -289,6 +289,10 @@ def run_experiment(hparams, run_opts, datasets): ) logger.info(datasets_summary) hparams["ch_positions"] = torch.from_numpy(datasets["ch_positions"]) + if ( + "normalize" in hparams + ): # TODO: Use a different normalizer for data vs. positions + hparams["ch_positions"] = hparams["normalize"](hparams["ch_positions"]) brain = MOABBBrain( modules={"model": hparams["model"]}, From c45e1d288e378deafdc2ce46f388f17023d5e766 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 3 Jul 2024 16:59:06 -0400 Subject: [PATCH 039/106] break up scripts --- benchmarks/MOABB/scripts/slurm_cp_dataset.sh | 2 ++ benchmarks/MOABB/scripts/slurm_hparam_opt.sh | 8 ++++++++ benchmarks/MOABB/scripts/slurm_train_base.sh | 4 +--- 3 files changed, 11 insertions(+), 3 deletions(-) create mode 100644 benchmarks/MOABB/scripts/slurm_cp_dataset.sh create mode 100755 benchmarks/MOABB/scripts/slurm_hparam_opt.sh diff --git a/benchmarks/MOABB/scripts/slurm_cp_dataset.sh b/benchmarks/MOABB/scripts/slurm_cp_dataset.sh new file mode 100644 index 000000000..b4a99673e --- /dev/null +++ b/benchmarks/MOABB/scripts/slurm_cp_dataset.sh @@ -0,0 +1,2 @@ +mkdir -p $SLURM_TMPDIR/mne_data +tar -axf $HOME/projects/def-ravanelm/datasets-open/$1.tar -C $SLURM_TMPDIR/mne_data diff --git a/benchmarks/MOABB/scripts/slurm_hparam_opt.sh b/benchmarks/MOABB/scripts/slurm_hparam_opt.sh new file mode 100755 index 000000000..57c4c6596 --- /dev/null +++ b/benchmarks/MOABB/scripts/slurm_hparam_opt.sh @@ -0,0 +1,8 @@ +source $HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_install_env.sh +$HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_cp_dataset.sh $dataset_tag + +cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB +./run_hparam_optimization.sh $@ \ + --output_folder $SLURM_TMPDIR/output_$SLURM_JOBID \ + --data_folder $SLURM_TMPDIR/mne_data/ +tar -caf $output_folder/results.tar.gz -C $SLURM_TMPDIR $SLURM_TMPDIR/output_$SLURM_JOBID diff --git a/benchmarks/MOABB/scripts/slurm_train_base.sh b/benchmarks/MOABB/scripts/slurm_train_base.sh index 382e3f267..166038b26 100755 --- a/benchmarks/MOABB/scripts/slurm_train_base.sh +++ b/benchmarks/MOABB/scripts/slurm_train_base.sh @@ -1,7 +1,5 @@ source $HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_install_env.sh - -mkdir -p $SLURM_TMPDIR/mne_data -tar -axf $HOME/projects/def-ravanelm/datasets-open/${dataset_tag}.tar -C $SLURM_TMPDIR/mne_data +$HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_cp_dataset.sh $dataset_tag cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB python train.py $@ --data_folder=$SLURM_TMPDIR/mne_data --cached_data_folder=$SLURM_TMPDIR/mne_data/pkl From 7eeaf27330da2f83e559e7c934ff7434e230c6a3 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Fri, 5 Jul 2024 11:46:11 -0400 Subject: [PATCH 040/106] add interpretter commend --- benchmarks/MOABB/scripts/slurm_hparam_opt.sh | 1 + 1 file changed, 1 insertion(+) diff --git a/benchmarks/MOABB/scripts/slurm_hparam_opt.sh b/benchmarks/MOABB/scripts/slurm_hparam_opt.sh index 57c4c6596..39f8db285 100755 --- a/benchmarks/MOABB/scripts/slurm_hparam_opt.sh +++ b/benchmarks/MOABB/scripts/slurm_hparam_opt.sh @@ -1,3 +1,4 @@ +#!/bin/sh source $HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_install_env.sh $HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_cp_dataset.sh $dataset_tag From 973000b1ba0f9f24c0279d2b1ad1202ee648d3e8 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Fri, 5 Jul 2024 16:59:48 -0400 Subject: [PATCH 041/106] activate env --- benchmarks/MOABB/scripts/slurm_hparam_opt.sh | 1 + 1 file changed, 1 insertion(+) diff --git a/benchmarks/MOABB/scripts/slurm_hparam_opt.sh b/benchmarks/MOABB/scripts/slurm_hparam_opt.sh index 39f8db285..1265a4626 100755 --- a/benchmarks/MOABB/scripts/slurm_hparam_opt.sh +++ b/benchmarks/MOABB/scripts/slurm_hparam_opt.sh @@ -1,5 +1,6 @@ #!/bin/sh source $HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_install_env.sh +source $SLURM_TMPDIR/venv/bin/activate $HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_cp_dataset.sh $dataset_tag cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB From 9489c13dab1e70f320d34351e73c8beb424b63dc Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Fri, 5 Jul 2024 17:10:34 -0400 Subject: [PATCH 042/106] module load --- benchmarks/MOABB/scripts/slurm_hparam_opt.sh | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/benchmarks/MOABB/scripts/slurm_hparam_opt.sh b/benchmarks/MOABB/scripts/slurm_hparam_opt.sh index 1265a4626..cbbd8b2d3 100755 --- a/benchmarks/MOABB/scripts/slurm_hparam_opt.sh +++ b/benchmarks/MOABB/scripts/slurm_hparam_opt.sh @@ -1,5 +1,9 @@ #!/bin/sh -source $HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_install_env.sh +$HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_install_env.sh +module load StdEnv/2020 +module load python/3.10.2 +module load scipy-stack + source $SLURM_TMPDIR/venv/bin/activate $HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_cp_dataset.sh $dataset_tag From 0a8a5c38ab0a02b1fe7e6f4d225a9a6d1c29cd7c Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Fri, 5 Jul 2024 17:14:12 -0400 Subject: [PATCH 043/106] make executable --- benchmarks/MOABB/scripts/slurm_cp_dataset.sh | 0 1 file changed, 0 insertions(+), 0 deletions(-) mode change 100644 => 100755 benchmarks/MOABB/scripts/slurm_cp_dataset.sh diff --git a/benchmarks/MOABB/scripts/slurm_cp_dataset.sh b/benchmarks/MOABB/scripts/slurm_cp_dataset.sh old mode 100644 new mode 100755 From 430fb4c6fa44ea56c5d5e61bafa21493fb5c7309 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Fri, 5 Jul 2024 17:21:21 -0400 Subject: [PATCH 044/106] update env names --- benchmarks/MOABB/scripts/slurm_hparam_opt.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/benchmarks/MOABB/scripts/slurm_hparam_opt.sh b/benchmarks/MOABB/scripts/slurm_hparam_opt.sh index cbbd8b2d3..db758809d 100755 --- a/benchmarks/MOABB/scripts/slurm_hparam_opt.sh +++ b/benchmarks/MOABB/scripts/slurm_hparam_opt.sh @@ -5,10 +5,10 @@ module load python/3.10.2 module load scipy-stack source $SLURM_TMPDIR/venv/bin/activate -$HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_cp_dataset.sh $dataset_tag +$HOME/speechbrain-benchmarks-private/benchmarks/MOABB/scripts/slurm_cp_dataset.sh $DATASET_TAG cd $HOME/speechbrain-benchmarks-private/benchmarks/MOABB ./run_hparam_optimization.sh $@ \ --output_folder $SLURM_TMPDIR/output_$SLURM_JOBID \ --data_folder $SLURM_TMPDIR/mne_data/ -tar -caf $output_folder/results.tar.gz -C $SLURM_TMPDIR $SLURM_TMPDIR/output_$SLURM_JOBID +tar -caf $OUTPUT_FOLDER/results.tar.gz -C $SLURM_TMPDIR $SLURM_TMPDIR/output_$SLURM_JOBID From 265f9973698d8c6272902911722b86444965e27b Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Mon, 8 Jul 2024 16:30:43 -0400 Subject: [PATCH 045/106] write with einsum instead --- benchmarks/MOABB/models/SpatialFocus.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/benchmarks/MOABB/models/SpatialFocus.py b/benchmarks/MOABB/models/SpatialFocus.py index 452a477e0..f3ed68c22 100644 --- a/benchmarks/MOABB/models/SpatialFocus.py +++ b/benchmarks/MOABB/models/SpatialFocus.py @@ -7,12 +7,14 @@ def __init__( self, n_focal_points, focus_dims=3, + knn=None, similarity_func="cosine", - similarity_transform=nn.Softmax(-2), + similarity_transform=nn.Softmax(0), ): super().__init__() self.n_focal_points = n_focal_points self.focus_dims = focus_dims + self.knn = knn self.similarity_func = similarity_func self.similarity_transform = similarity_transform @@ -36,12 +38,5 @@ def forward(self, x: torch.Tensor, positions: torch.Tensor): else: weights = similarity - weights = weights.unsqueeze(0) # Batch dimension - if x.dim() == 4: - weights = weights.unsqueeze(1) # Time dimension - weights = weights.unsqueeze(-1) # Feature dimension - - x = x.unsqueeze(-2) # Focus dimension - x = (x * weights).sum(dim=-3) - + x = torch.einsum("...cf, cd -> ...df", x, weights) return x From 274e5af9bd326af31579ca276216d918035f7f74 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 9 Jul 2024 08:29:08 -0400 Subject: [PATCH 046/106] remove knn --- benchmarks/MOABB/models/SpatialFocus.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/benchmarks/MOABB/models/SpatialFocus.py b/benchmarks/MOABB/models/SpatialFocus.py index f3ed68c22..7f55322e6 100644 --- a/benchmarks/MOABB/models/SpatialFocus.py +++ b/benchmarks/MOABB/models/SpatialFocus.py @@ -7,14 +7,12 @@ def __init__( self, n_focal_points, focus_dims=3, - knn=None, similarity_func="cosine", similarity_transform=nn.Softmax(0), ): super().__init__() self.n_focal_points = n_focal_points self.focus_dims = focus_dims - self.knn = knn self.similarity_func = similarity_func self.similarity_transform = similarity_transform From 5c70369b81cf62fde7257d25a31008a3b36ad19a Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 9 Jul 2024 08:42:30 -0400 Subject: [PATCH 047/106] use native pytorch cosine similarity --- benchmarks/MOABB/models/SpatialFocus.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/benchmarks/MOABB/models/SpatialFocus.py b/benchmarks/MOABB/models/SpatialFocus.py index 7f55322e6..6b04afa88 100644 --- a/benchmarks/MOABB/models/SpatialFocus.py +++ b/benchmarks/MOABB/models/SpatialFocus.py @@ -1,5 +1,5 @@ import torch -from torch import nn +import torch.nn as nn class SpatialFocus(nn.Module): @@ -24,9 +24,8 @@ def forward(self, x: torch.Tensor, positions: torch.Tensor): focal_points = self.focal_points.weight if self.similarity_func == "cosine": - similarity = (positions @ focal_points.T) / ( - positions.norm(dim=-1).unsqueeze(1) - @ focal_points.norm(dim=-1).unsqueeze(0) + similarity = nn.functional.cosine_similarity( + positions.unsqueeze(1), focal_points.unsqueeze(0), dim=-1 ) else: similarity = self.similarity_func(positions, focal_points) From d784c090689c6949d78664c20d26d63f12973fe3 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 10 Jul 2024 10:55:11 -0400 Subject: [PATCH 048/106] add temperature softmax --- .../BNCI2014001/EEGNet_with_spatial_focus.yaml | 2 ++ benchmarks/MOABB/models/EEGNet_spatial_focus.py | 12 +++++++++++- 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml index 275987daa..1b505b735 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml @@ -130,6 +130,7 @@ normalize: !name:speechbrain.processing.signal_processing.mean_std_norm dims: !ref # MODEL number_of_foci: 22 # @orion_step1: --number_of_foci~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 1.0 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0)" input_shape: [null, !ref , !ref , null] cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(4, 64,discrete=True)" cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" @@ -167,6 +168,7 @@ model: !new:models.EEGNet_spatial_focus.EEGNet cnn_septemporal_pool: [!ref , 1] cnn_pool_type: !ref activation_type: !ref + spatial_focus_tau: !ref dense_max_norm: !ref dropout: !ref dense_n_neurons: !ref diff --git a/benchmarks/MOABB/models/EEGNet_spatial_focus.py b/benchmarks/MOABB/models/EEGNet_spatial_focus.py index 5636b14ae..bba31f29b 100644 --- a/benchmarks/MOABB/models/EEGNet_spatial_focus.py +++ b/benchmarks/MOABB/models/EEGNet_spatial_focus.py @@ -70,6 +70,7 @@ def __init__( dense_max_norm=0.25, dense_n_neurons=4, activation_type="elu", + spatial_focus_tau=1.0, ): super().__init__() if input_shape is None: @@ -111,7 +112,16 @@ def __init__( input_size=cnn_temporal_kernels, momentum=0.01, affine=True, ), ) - self.spatial_focus = SpatialFocus(n_focal_points=C, focus_dims=3) + self.spatial_focus = ( + SpatialFocus( + similarity_func="cosine", + similarity_transform=sb.nnet.activations.GumbelSoftmax( + spatial_focus_tau, dim=0 + ), + n_focal_points=C, + focus_dims=3, + ), + ) self.conv_module = torch.nn.Sequential() # Spatial depthwise convolution cnn_spatial_kernels = ( From 431b82fb647e35710151db71ed9bf06c7d4169d3 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Thu, 18 Jul 2024 19:22:47 +0000 Subject: [PATCH 049/106] spatial focus experiments --- .gitignore | 6 +- .../EEGNet_with_spatial_focus.yaml | 8 +- ...EEGNet_with_spatial_focus_lim_hparams.yaml | 174 ++++++++++++++++++ .../MOABB/models/EEGNet_spatial_focus.py | 12 +- 4 files changed, 189 insertions(+), 11 deletions(-) create mode 100644 benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus_lim_hparams.yaml diff --git a/.gitignore b/.gitignore index 6606d1085..0f73f4303 100644 --- a/.gitignore +++ b/.gitignore @@ -158,4 +158,8 @@ dmypy.json **/log/ # Mac OS -.DS_Store \ No newline at end of file +.DS_Store + +results +eeg_data +trial_*.conf \ No newline at end of file diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml index 1b505b735..61a1aa2cf 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml @@ -84,12 +84,12 @@ valid_ratio: 0.2 # DATA AUGMENTATION # cutcat (disabled when min_num_segments=max_num_segments=1) -max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +max_num_segments: 3 # orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" cutcat: !new:speechbrain.augment.time_domain.CutCat min_num_segments: 2 max_num_segments: !ref # random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) -amp_delta: 0.01742 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +amp_delta: 0.01742 # orion_step2: --amp_delta~"uniform(0.0, 0.5)" rand_amp: !new:speechbrain.augment.time_domain.RandAmp amp_low: !ref 1 - amp_high: !ref 1 + @@ -105,8 +105,8 @@ time_shift: !new:speechbrain.augment.freq_domain.RandomShift max_shift: !ref dim: 1 # injection of gaussian white noise -snr_white_low: 15.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" -snr_white_delta: 19.1 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_low: 15.0 # orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" snr_white_high: !ref + add_noise_white: !new:speechbrain.augment.time_domain.AddNoise snr_low: !ref diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus_lim_hparams.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus_lim_hparams.yaml new file mode 100644 index 000000000..a2ce985d6 --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus_lim_hparams.yaml @@ -0,0 +1,174 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: + !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder + + +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' + + +output_folder: !PLACEHOLDER + #'path/to/results' + + # DATASET HPARS + # Defining the MOABB dataset. + + +dataset: !new:moabb.datasets.BNCI2014001 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 250 # Original sampling rate provided by dataset authors +sample_rate: 125 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.1 # @orion_step2: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 50.0 # @orion_step2: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 4 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 4.0 # @orion_step2: --tmax~"uniform(1.0, 4.0, precision=2)" +n_steps_channel_selection: null +T: !apply:math.ceil + - !ref * ( - ) +C: 22 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: "last" # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: "macro" +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 1 # @orion_step2: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 250 # @orion_step2: --number_of_epochs~"uniform(250, 1000, discrete=True)" +lr: 0.0001 # @orion_step2: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step3: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.01742 # @orion_step3: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 1 # @orion_step3: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 15.0 # @orion_step3: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # @orion_step3: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +# MODEL +number_of_foci: 22 # @orion_step1: --number_of_foci~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 1.0 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0)" +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(4, 64,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.EEGNet_spatial_focus.EEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus_tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref diff --git a/benchmarks/MOABB/models/EEGNet_spatial_focus.py b/benchmarks/MOABB/models/EEGNet_spatial_focus.py index bba31f29b..2277120e0 100644 --- a/benchmarks/MOABB/models/EEGNet_spatial_focus.py +++ b/benchmarks/MOABB/models/EEGNet_spatial_focus.py @@ -5,6 +5,7 @@ Authors * Davide Borra, 2021 """ +from functools import partial import torch from models.SpatialFocus import SpatialFocus import speechbrain as sb @@ -112,16 +113,15 @@ def __init__( input_size=cnn_temporal_kernels, momentum=0.01, affine=True, ), ) - self.spatial_focus = ( - SpatialFocus( + self.spatial_focus = SpatialFocus( similarity_func="cosine", - similarity_transform=sb.nnet.activations.GumbelSoftmax( - spatial_focus_tau, dim=0 + similarity_transform=partial( + torch.nn.functional.gumbel_softmax, + tau=spatial_focus_tau, dim=0 ), n_focal_points=C, focus_dims=3, - ), - ) + ) self.conv_module = torch.nn.Sequential() # Spatial depthwise convolution cnn_spatial_kernels = ( From 805bcfb9c8d9038bb39e123517b7b31bc6c7495e Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Thu, 18 Jul 2024 21:38:07 +0000 Subject: [PATCH 050/106] fixed spatial focus (can now train to near full accuracy) --- .../EEGNet_with_spatial_focus.yaml | 45 +++-- ...EEGNet_with_spatial_focus_lim_hparams.yaml | 174 ------------------ .../MOABB/models/EEGNet_spatial_focus.py | 27 ++- benchmarks/MOABB/models/SpatialFocus.py | 41 ++--- benchmarks/MOABB/train.py | 12 +- 5 files changed, 60 insertions(+), 239 deletions(-) delete mode 100644 benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus_lim_hparams.yaml diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml index 61a1aa2cf..e0cd52fa4 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml @@ -25,15 +25,15 @@ events_to_load: null # all events will be loaded original_sample_rate: 250 # Original sampling rate provided by dataset authors sample_rate: 125 # Target sampling rate (Hz) # band-pass filtering cut-off frequencies -fmin: 0.13 # @orion_step1: --fmin~"uniform(0.1, 5, precision=2)" -fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +fmin: 0.13 # @orion_step0: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step0: --fmax~"uniform(20.0, 50.0, precision=3)" n_classes: 4 # tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class # trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long # dataset interval starts from 2 # -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) tmin: 0. -tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +tmax: 4.0 # @orion_step0: --tmax~"uniform(1.0, 4.0, precision=2)" n_steps_channel_selection: null T: !apply:math.ceil - !ref * ( - ) @@ -58,9 +58,9 @@ metrics: # TRAINING HPARS n_train_examples: 100 # it will be replaced in the train script # checkpoints to average -avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 862 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" -lr: 0.0001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +avg_models: 10 # @orion_step0: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 862 # @orion_step0: --number_of_epochs~"uniform(250, 1000, discrete=True)" +lr: 0.0001 # @orion_step0: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) @@ -78,7 +78,7 @@ optimizer: !name:torch.optim.Adam lr: !ref epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter limit: !ref -batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size_exponent: 4 # @orion_step0: --batch_size_exponent~"uniform(4, 6,discrete=True)" batch_size: !ref 2 ** valid_ratio: 0.2 @@ -112,7 +112,7 @@ add_noise_white: !new:speechbrain.augment.time_domain.AddNoise snr_low: !ref snr_high: !ref -repeat_augment: 1 # @orion_step1: --repeat_augment 0 +repeat_augment: 1 # @orion_step0: --repeat_augment 0 augment: !new:speechbrain.augment.augmenter.Augmenter parallel_augment: True concat_original: True @@ -129,30 +129,32 @@ dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) normalize: !name:speechbrain.processing.signal_processing.mean_std_norm dims: !ref # MODEL -number_of_foci: 22 # @orion_step1: --number_of_foci~"uniform(4, 44, discrete=True)" -spatial_focus_tau: 1.0 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0)" -input_shape: [null, !ref , !ref , null] -cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(4, 64,discrete=True)" -cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.1, 1.0)" +spatial_focus_sigma: 0 # @orion_step1: --spatial_focus_sigma~"uniform(0.0, 0.1, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step0: --cnn_temporal_kernels~"uniform(4, 64,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step0: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" # depth multiplier for the spatial depthwise conv. layer -cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_depth_multiplier: 4 # @orion_step0: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer cnn_spatial_pool: 4 cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer -cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step0: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" cnn_septemporal_point_kernels_ratio: !ref / 4 ## number of temporal filters in the separable temporal conv. layer cnn_septemporal_point_kernels_: !ref * * cnn_septemporal_point_kernels: !apply:math.ceil - !ref * + 1 -cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +cnn_septemporal_kernelsize_: 15 # @orion_step0: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" max_cnn_spatial_pool: 4 cnn_septemporal_kernelsize: !apply:round - !ref * / -cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_septemporal_pool: 7 # @orion_step0: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" cnn_pool_type: "avg" dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer -dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +dropout: 0.008464 # @orion_step0: --dropout~"uniform(0.0, 0.5)" activation_type: "elu" model: !new:models.EEGNet_spatial_focus.EEGNet @@ -168,7 +170,12 @@ model: !new:models.EEGNet_spatial_focus.EEGNet cnn_septemporal_pool: [!ref , 1] cnn_pool_type: !ref activation_type: !ref - spatial_focus_tau: !ref + spatial_focus: !new:models.SpatialFocus.SpatialFocus + projection_dim: !ref + position_dim: 3 + sigma: !ref + tau: !ref dense_max_norm: !ref dropout: !ref dense_n_neurons: !ref + diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus_lim_hparams.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus_lim_hparams.yaml deleted file mode 100644 index a2ce985d6..000000000 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus_lim_hparams.yaml +++ /dev/null @@ -1,174 +0,0 @@ -seed: 1234 -__set_torchseed: !apply:torch.manual_seed [!ref ] - -# DIRECTORIES -data_folder: - !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder - - -cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' - - -output_folder: !PLACEHOLDER - #'path/to/results' - - # DATASET HPARS - # Defining the MOABB dataset. - - -dataset: !new:moabb.datasets.BNCI2014001 -save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards -data_iterator_name: !PLACEHOLDER -target_subject_idx: !PLACEHOLDER -target_session_idx: !PLACEHOLDER -events_to_load: null # all events will be loaded -original_sample_rate: 250 # Original sampling rate provided by dataset authors -sample_rate: 125 # Target sampling rate (Hz) -# band-pass filtering cut-off frequencies -fmin: 0.1 # @orion_step2: --fmin~"uniform(0.1, 5, precision=2)" -fmax: 50.0 # @orion_step2: --fmax~"uniform(20.0, 50.0, precision=3)" -n_classes: 4 -# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class -# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long -# dataset interval starts from 2 -# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) -tmin: 0. -tmax: 4.0 # @orion_step2: --tmax~"uniform(1.0, 4.0, precision=2)" -n_steps_channel_selection: null -T: !apply:math.ceil - - !ref * ( - ) -C: 22 -# We here specify how to perfom test: -# - If test_with: 'last' we perform test with the latest model. -# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) -# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. -# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. -test_with: "last" # 'last' or 'best' -test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" - -# METRICS -f1: !name:sklearn.metrics.f1_score - average: "macro" -acc: !name:sklearn.metrics.balanced_accuracy_score -cm: !name:sklearn.metrics.confusion_matrix -metrics: - f1: !ref - acc: !ref - cm: !ref -# TRAINING HPARS -n_train_examples: 100 # it will be replaced in the train script -# checkpoints to average -avg_models: 1 # @orion_step2: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 250 # @orion_step2: --number_of_epochs~"uniform(250, 1000, discrete=True)" -lr: 0.0001 # @orion_step2: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" -# Learning rate scheduling (cyclic learning rate is used here) -max_lr: !ref # Upper bound of the cycle (max value of the lr) -base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) -step_size_multiplier: 5 #from 2 to 8 -step_size: !apply:round - - !ref * / -lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler - base_lr: !ref - max_lr: !ref - step_size: !ref -label_smoothing: 0.0 -loss: !name:speechbrain.nnet.losses.nll_loss - label_smoothing: !ref -optimizer: !name:torch.optim.Adam - lr: !ref -epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter - limit: !ref -batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" -batch_size: !ref 2 ** -valid_ratio: 0.2 - -# DATA AUGMENTATION -# cutcat (disabled when min_num_segments=max_num_segments=1) -max_num_segments: 3 # @orion_step3: --max_num_segments~"uniform(2, 6, discrete=True)" -cutcat: !new:speechbrain.augment.time_domain.CutCat - min_num_segments: 2 - max_num_segments: !ref -# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) -amp_delta: 0.01742 # @orion_step3: --amp_delta~"uniform(0.0, 0.5)" -rand_amp: !new:speechbrain.augment.time_domain.RandAmp - amp_low: !ref 1 - - amp_high: !ref 1 + -# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) -shift_delta_: 1 # @orion_step3: --shift_delta_~"uniform(0, 25, discrete=True)" -shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 -min_shift: !apply:math.floor - - !ref 0 - * -max_shift: !apply:math.floor - - !ref 0 + * -time_shift: !new:speechbrain.augment.freq_domain.RandomShift - min_shift: !ref - max_shift: !ref - dim: 1 -# injection of gaussian white noise -snr_white_low: 15.0 # @orion_step3: --snr_white_low~"uniform(0.0, 15, precision=2)" -snr_white_delta: 19.1 # @orion_step3: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" -snr_white_high: !ref + -add_noise_white: !new:speechbrain.augment.time_domain.AddNoise - snr_low: !ref - snr_high: !ref - -repeat_augment: 1 # @orion_step1: --repeat_augment 0 -augment: !new:speechbrain.augment.augmenter.Augmenter - parallel_augment: True - concat_original: True - parallel_augment_fixed_bs: True - repeat_augment: !ref - shuffle_augmentations: True - min_augmentations: 4 - max_augmentations: 4 - augmentations: - [!ref , !ref , !ref , !ref ] - -# DATA NORMALIZATION -dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) -normalize: !name:speechbrain.processing.signal_processing.mean_std_norm - dims: !ref -# MODEL -number_of_foci: 22 # @orion_step1: --number_of_foci~"uniform(4, 44, discrete=True)" -spatial_focus_tau: 1.0 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0)" -input_shape: [null, !ref , !ref , null] -cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(4, 64,discrete=True)" -cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" -# depth multiplier for the spatial depthwise conv. layer -cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" -cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer -cnn_spatial_pool: 4 -cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer -cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" -cnn_septemporal_point_kernels_ratio: !ref / 4 -## number of temporal filters in the separable temporal conv. layer -cnn_septemporal_point_kernels_: !ref * * -cnn_septemporal_point_kernels: !apply:math.ceil - - !ref * + 1 -cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" -max_cnn_spatial_pool: 4 -cnn_septemporal_kernelsize: !apply:round - - !ref * / -cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" -cnn_pool_type: "avg" -dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer -dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" -activation_type: "elu" - -model: !new:models.EEGNet_spatial_focus.EEGNet - input_shape: !ref - cnn_temporal_kernels: !ref - cnn_temporal_kernelsize: [!ref , 1] - cnn_spatial_depth_multiplier: !ref - cnn_spatial_max_norm: !ref - cnn_spatial_pool: [!ref , 1] - cnn_septemporal_depth_multiplier: !ref - cnn_septemporal_point_kernels: !ref - cnn_septemporal_kernelsize: [!ref , 1] - cnn_septemporal_pool: [!ref , 1] - cnn_pool_type: !ref - activation_type: !ref - spatial_focus_tau: !ref - dense_max_norm: !ref - dropout: !ref - dense_n_neurons: !ref diff --git a/benchmarks/MOABB/models/EEGNet_spatial_focus.py b/benchmarks/MOABB/models/EEGNet_spatial_focus.py index 2277120e0..2de68fd4d 100644 --- a/benchmarks/MOABB/models/EEGNet_spatial_focus.py +++ b/benchmarks/MOABB/models/EEGNet_spatial_focus.py @@ -5,6 +5,7 @@ Authors * Davide Borra, 2021 """ + from functools import partial import torch from models.SpatialFocus import SpatialFocus @@ -71,7 +72,7 @@ def __init__( dense_max_norm=0.25, dense_n_neurons=4, activation_type="elu", - spatial_focus_tau=1.0, + spatial_focus=None, ): super().__init__() if input_shape is None: @@ -110,18 +111,12 @@ def __init__( self.temporal_frontend.add_module( "bnorm_0", sb.nnet.normalization.BatchNorm2d( - input_size=cnn_temporal_kernels, momentum=0.01, affine=True, + input_size=cnn_temporal_kernels, + momentum=0.01, + affine=True, ), ) - self.spatial_focus = SpatialFocus( - similarity_func="cosine", - similarity_transform=partial( - torch.nn.functional.gumbel_softmax, - tau=spatial_focus_tau, dim=0 - ), - n_focal_points=C, - focus_dims=3, - ) + self.spatial_focus = spatial_focus self.conv_module = torch.nn.Sequential() # Spatial depthwise convolution cnn_spatial_kernels = ( @@ -143,7 +138,9 @@ def __init__( self.conv_module.add_module( "bnorm_1", sb.nnet.normalization.BatchNorm2d( - input_size=cnn_spatial_kernels, momentum=0.01, affine=True, + input_size=cnn_spatial_kernels, + momentum=0.01, + affine=True, ), ) self.conv_module.add_module("act_1", activation) @@ -215,7 +212,8 @@ def __init__( # DENSE MODULE self.dense_module = torch.nn.Sequential() self.dense_module.add_module( - "flatten", torch.nn.Flatten(), + "flatten", + torch.nn.Flatten(), ) self.dense_module.add_module( "fc_out", @@ -257,7 +255,8 @@ def forward(self, x, positions=None): if positions is None: positions = torch.zeros((x.shape[-2], 3), device=x.device) x = self.temporal_frontend(x) - x = self.spatial_focus(x, positions) + if self.spatial_focus is not None: + x = self.spatial_focus(x, positions) x = self.conv_module(x) x = self.dense_module(x) return x diff --git a/benchmarks/MOABB/models/SpatialFocus.py b/benchmarks/MOABB/models/SpatialFocus.py index 6b04afa88..c291e1f34 100644 --- a/benchmarks/MOABB/models/SpatialFocus.py +++ b/benchmarks/MOABB/models/SpatialFocus.py @@ -3,37 +3,24 @@ class SpatialFocus(nn.Module): - def __init__( - self, - n_focal_points, - focus_dims=3, - similarity_func="cosine", - similarity_transform=nn.Softmax(0), - ): + def __init__(self, projection_dim, position_dim=3, tau=1.0, sigma=0.0): super().__init__() - self.n_focal_points = n_focal_points - self.focus_dims = focus_dims - self.similarity_func = similarity_func - self.similarity_transform = similarity_transform + self.projection_dim = projection_dim + self.position_dim = position_dim + self.tau = tau + self.sigma = sigma - self.focal_points = nn.Embedding( - num_embeddings=n_focal_points, embedding_dim=self.focus_dims + self.similarity_module = nn.Sequential( + nn.Linear(position_dim, projection_dim), + nn.ELU(), + nn.Linear(projection_dim, projection_dim), ) + self.softmax = nn.Softmax(dim=-2) def forward(self, x: torch.Tensor, positions: torch.Tensor): - focal_points = self.focal_points.weight - - if self.similarity_func == "cosine": - similarity = nn.functional.cosine_similarity( - positions.unsqueeze(1), focal_points.unsqueeze(0), dim=-1 - ) - else: - similarity = self.similarity_func(positions, focal_points) - - if self.similarity_transform: - weights = self.similarity_transform(similarity) - else: - weights = similarity - + if self.training and self.sigma > 0: + positions = positions + torch.randn_like(positions) * self.sigma + weights = self.similarity_module(positions) + weights = self.softmax(weights / self.tau) x = torch.einsum("...cf, cd -> ...df", x, weights) return x diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 71c26f63b..3617d398a 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -39,6 +39,9 @@ def init_model(self, model): if mod.bias is not None: init.constant_(mod.bias, 0) + # foo = torch.load('foo.torch') + # model.spatial_focus.similarity_module.load_state_dict(foo) + def compute_forward(self, batch, stage): "Given an input batch it computes the model output." inputs = batch[0].to(self.device) @@ -288,11 +291,10 @@ def run_experiment(hparams, run_opts, datasets): datasets["test"].dataset.tensors[0].shape[0], ) logger.info(datasets_summary) - hparams["ch_positions"] = torch.from_numpy(datasets["ch_positions"]) - if ( - "normalize" in hparams - ): # TODO: Use a different normalizer for data vs. positions - hparams["ch_positions"] = hparams["normalize"](hparams["ch_positions"]) + ch_positions = datasets["ch_positions"] + hparams["ch_positions"] = torch.from_numpy( + 2 * ((ch_positions - ch_positions.min(0)) / (ch_positions.max(0) - ch_positions.min(0)) - 0.5) + ) brain = MOABBBrain( modules={"model": hparams["model"]}, From daa1ed2d2687eef4210325ae803b740bd6214c30 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 23 Jul 2024 13:41:43 +0000 Subject: [PATCH 051/106] adjust priors --- .../EEGNet_with_spatial_focus.yaml | 32 +++++++++---------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml index e0cd52fa4..842a12296 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml @@ -25,15 +25,15 @@ events_to_load: null # all events will be loaded original_sample_rate: 250 # Original sampling rate provided by dataset authors sample_rate: 125 # Target sampling rate (Hz) # band-pass filtering cut-off frequencies -fmin: 0.13 # @orion_step0: --fmin~"uniform(0.1, 5, precision=2)" -fmax: 46.0 # @orion_step0: --fmax~"uniform(20.0, 50.0, precision=3)" +fmin: 0.13 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" n_classes: 4 # tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class # trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long # dataset interval starts from 2 # -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) tmin: 0. -tmax: 4.0 # @orion_step0: --tmax~"uniform(1.0, 4.0, precision=2)" +tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" n_steps_channel_selection: null T: !apply:math.ceil - !ref * ( - ) @@ -58,9 +58,9 @@ metrics: # TRAINING HPARS n_train_examples: 100 # it will be replaced in the train script # checkpoints to average -avg_models: 10 # @orion_step0: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 862 # @orion_step0: --number_of_epochs~"uniform(250, 1000, discrete=True)" -lr: 0.0001 # @orion_step0: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 862 # @orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) @@ -78,7 +78,7 @@ optimizer: !name:torch.optim.Adam lr: !ref epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter limit: !ref -batch_size_exponent: 4 # @orion_step0: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" batch_size: !ref 2 ** valid_ratio: 0.2 @@ -112,7 +112,7 @@ add_noise_white: !new:speechbrain.augment.time_domain.AddNoise snr_low: !ref snr_high: !ref -repeat_augment: 1 # @orion_step0: --repeat_augment 0 +repeat_augment: 1 # @orion_step1: --repeat_augment 0 augment: !new:speechbrain.augment.augmenter.Augmenter parallel_augment: True concat_original: True @@ -130,31 +130,31 @@ normalize: !name:speechbrain.processing.signal_processing.mean_std_norm dims: !ref # MODEL projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" -spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.1, 1.0)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.01, 1.0, precision=4)" spatial_focus_sigma: 0 # @orion_step1: --spatial_focus_sigma~"uniform(0.0, 0.1, precision=4)" input_shape: [null, !ref , !ref , null] -cnn_temporal_kernels: 61 # @orion_step0: --cnn_temporal_kernels~"uniform(4, 64,discrete=True)" -cnn_temporal_kernelsize: 51 # @orion_step0: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" # depth multiplier for the spatial depthwise conv. layer -cnn_spatial_depth_multiplier: 4 # @orion_step0: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer cnn_spatial_pool: 4 cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer -cnn_septemporal_point_kernels_ratio_: 7 # @orion_step0: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" cnn_septemporal_point_kernels_ratio: !ref / 4 ## number of temporal filters in the separable temporal conv. layer cnn_septemporal_point_kernels_: !ref * * cnn_septemporal_point_kernels: !apply:math.ceil - !ref * + 1 -cnn_septemporal_kernelsize_: 15 # @orion_step0: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" max_cnn_spatial_pool: 4 cnn_septemporal_kernelsize: !apply:round - !ref * / -cnn_septemporal_pool: 7 # @orion_step0: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" cnn_pool_type: "avg" dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer -dropout: 0.008464 # @orion_step0: --dropout~"uniform(0.0, 0.5)" +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" activation_type: "elu" model: !new:models.EEGNet_spatial_focus.EEGNet From 067dcecc32dace124244dffbcf4ad60659f9e1aa Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 23 Jul 2024 13:41:53 +0000 Subject: [PATCH 052/106] fix leave-one-subject-out --- benchmarks/MOABB/utils/dataio_iterators.py | 38 +++++++++++++++++----- 1 file changed, 29 insertions(+), 9 deletions(-) diff --git a/benchmarks/MOABB/utils/dataio_iterators.py b/benchmarks/MOABB/utils/dataio_iterators.py index bec4ec929..a875f3da2 100644 --- a/benchmarks/MOABB/utils/dataio_iterators.py +++ b/benchmarks/MOABB/utils/dataio_iterators.py @@ -604,19 +604,32 @@ def __init__(self, seed): def get_data( self, - *, + target_subject_idx: int, + target_session_idx: Optional[int], + valid_ratio: float, dataset: MOABBDataset, - target_subject_idx=None, - target_session_idx=None, # noqa - valid_ratio=None, - **prepare_data_kwargs, + data_folder: Optional[str], + cached_data_folder: Optional[str], + original_sample_rate: int, + sample_rate: Optional[int], + fmin: Optional[float], + fmax: Optional[float], + events_to_load: Optional[List[str]], + save_prepared_dataset: bool, ): self._validate_dataset_or_raise(dataset) # preparing or loading test set target_data_dict = prepare_data( - dataset=dataset, idx_subject_to_prepare=target_subject_idx, - **prepare_data_kwargs, + dataset=dataset, + data_folder=data_folder, + cached_data_folder=cached_data_folder, + srate_in=original_sample_rate, + srate_out=sample_rate, + fmin=fmin, + fmax=fmax, + save_prepared_dataset=save_prepared_dataset, + events_to_load=events_to_load, ) x_test = target_data_dict["x"] @@ -646,9 +659,16 @@ def get_data( for subject_idx in subject_idx_train: # preparing or loading training/valid set data_dict = prepare_data( - dataset=dataset, idx_subject_to_prepare=subject_idx, - **prepare_data_kwargs, + dataset=dataset, + data_folder=data_folder, + cached_data_folder=cached_data_folder, + srate_in=original_sample_rate, + srate_out=sample_rate, + fmin=fmin, + fmax=fmax, + save_prepared_dataset=save_prepared_dataset, + events_to_load=events_to_load, ) tmp_x_train = data_dict["x"] From 96c01b4d48a41f130c3cbeb8fe607874c1f11570 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 23 Jul 2024 13:59:02 +0000 Subject: [PATCH 053/106] refactor to expose encoder --- benchmarks/MOABB/models/EEGNet_spatial_focus.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/benchmarks/MOABB/models/EEGNet_spatial_focus.py b/benchmarks/MOABB/models/EEGNet_spatial_focus.py index 2de68fd4d..d2c816301 100644 --- a/benchmarks/MOABB/models/EEGNet_spatial_focus.py +++ b/benchmarks/MOABB/models/EEGNet_spatial_focus.py @@ -6,9 +6,7 @@ * Davide Borra, 2021 """ -from functools import partial import torch -from models.SpatialFocus import SpatialFocus import speechbrain as sb @@ -57,6 +55,7 @@ class EEGNet(torch.nn.Module): def __init__( self, + spatial_focus, input_shape=None, # (1, T, C, 1) cnn_temporal_kernels=8, cnn_temporal_kernelsize=(33, 1), @@ -72,7 +71,6 @@ def __init__( dense_max_norm=0.25, dense_n_neurons=4, activation_type="elu", - spatial_focus=None, ): super().__init__() if input_shape is None: @@ -207,6 +205,12 @@ def __init__( ) self.conv_module.add_module("dropout_3", torch.nn.Dropout(p=dropout)) + self.encoder = torch.nn.Sequential( + self.temporal_frontend, + self.spatial_focus, + self.conv_module + ) + # Shape of intermediate feature maps dense_input_size = self._num_flat_features(input_shape) # DENSE MODULE @@ -254,9 +258,6 @@ def forward(self, x, positions=None): """ if positions is None: positions = torch.zeros((x.shape[-2], 3), device=x.device) - x = self.temporal_frontend(x) - if self.spatial_focus is not None: - x = self.spatial_focus(x, positions) - x = self.conv_module(x) + x = self.encoder(x) x = self.dense_module(x) return x From 4bab70f092eb130b0c5079fc587d0488afaa8545 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 30 Jul 2024 12:34:47 -0400 Subject: [PATCH 054/106] rename -> SpatialEEGNet --- ..._spatial_focus.yaml => SpatialEEGNet.yaml} | 6 +- ...{EEGNet_spatial_focus.py => SpatialEEGNet} | 57 +++++++++++++------ benchmarks/MOABB/models/SpatialFocus.py | 26 --------- 3 files changed, 43 insertions(+), 46 deletions(-) rename benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/{EEGNet_with_spatial_focus.yaml => SpatialEEGNet.yaml} (98%) rename benchmarks/MOABB/models/{EEGNet_spatial_focus.py => SpatialEEGNet} (83%) delete mode 100644 benchmarks/MOABB/models/SpatialFocus.py diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml similarity index 98% rename from benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml rename to benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml index 842a12296..1c180c1cc 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet_with_spatial_focus.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -157,7 +157,7 @@ dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" activation_type: "elu" -model: !new:models.EEGNet_spatial_focus.EEGNet +model: !new:models.SpatialEEGNet.SpatialEEGNet input_shape: !ref cnn_temporal_kernels: !ref cnn_temporal_kernelsize: [!ref , 1] @@ -170,7 +170,7 @@ model: !new:models.EEGNet_spatial_focus.EEGNet cnn_septemporal_pool: [!ref , 1] cnn_pool_type: !ref activation_type: !ref - spatial_focus: !new:models.SpatialFocus.SpatialFocus + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus projection_dim: !ref position_dim: 3 sigma: !ref @@ -178,4 +178,4 @@ model: !new:models.EEGNet_spatial_focus.EEGNet dense_max_norm: !ref dropout: !ref dense_n_neurons: !ref - + diff --git a/benchmarks/MOABB/models/EEGNet_spatial_focus.py b/benchmarks/MOABB/models/SpatialEEGNet similarity index 83% rename from benchmarks/MOABB/models/EEGNet_spatial_focus.py rename to benchmarks/MOABB/models/SpatialEEGNet index d2c816301..44a5c8359 100644 --- a/benchmarks/MOABB/models/EEGNet_spatial_focus.py +++ b/benchmarks/MOABB/models/SpatialEEGNet @@ -7,10 +7,35 @@ """ import torch +import torch.nn as nn import speechbrain as sb -class EEGNet(torch.nn.Module): +class SpatialFocus(nn.Module): + def __init__(self, projection_dim, position_dim=3, tau=1.0, sigma=0.0): + super().__init__() + self.projection_dim = projection_dim + self.position_dim = position_dim + self.tau = tau + self.sigma = sigma + + self.similarity_module = nn.Sequential( + nn.Linear(position_dim, projection_dim), + nn.ELU(), + nn.Linear(projection_dim, projection_dim), + ) + self.softmax = nn.Softmax(dim=-2) + + def forward(self, x: torch.Tensor, positions: torch.Tensor): + if self.training and self.sigma > 0: + positions = positions + torch.randn_like(positions) * self.sigma + weights = self.similarity_module(positions) + weights = self.softmax(weights / self.tau) + x = torch.einsum("...cf, cd -> ...df", x, weights) + return x + + +class SpatialEEGNet(nn.Module): """EEGNet. Arguments @@ -76,15 +101,15 @@ def __init__( if input_shape is None: raise ValueError("Must specify input_shape") if activation_type == "gelu": - activation = torch.nn.GELU() + activation = nn.GELU() elif activation_type == "elu": - activation = torch.nn.ELU() + activation = nn.ELU() elif activation_type == "relu": - activation = torch.nn.ReLU() + activation = nn.ReLU() elif activation_type == "leaky_relu": - activation = torch.nn.LeakyReLU() + activation = nn.LeakyReLU() elif activation_type == "prelu": - activation = torch.nn.PReLU() + activation = nn.PReLU() else: raise ValueError("Wrong hidden activation function") self.default_sf = 128 # sampling rate of the original publication (Hz) @@ -92,7 +117,7 @@ def __init__( C = input_shape[2] # CONVOLUTIONAL MODULE - self.temporal_frontend = torch.nn.Sequential() + self.temporal_frontend = nn.Sequential() # Temporal convolution self.temporal_frontend.add_module( "conv_0", @@ -115,7 +140,7 @@ def __init__( ), ) self.spatial_focus = spatial_focus - self.conv_module = torch.nn.Sequential() + self.conv_module = nn.Sequential() # Spatial depthwise convolution cnn_spatial_kernels = ( cnn_spatial_depth_multiplier * cnn_temporal_kernels @@ -151,7 +176,7 @@ def __init__( pool_axis=[1, 2], ), ) - self.conv_module.add_module("dropout_1", torch.nn.Dropout(p=dropout)) + self.conv_module.add_module("dropout_1", nn.Dropout(p=dropout)) # Temporal separable convolution cnn_septemporal_kernels = ( @@ -203,21 +228,19 @@ def __init__( pool_axis=[1, 2], ), ) - self.conv_module.add_module("dropout_3", torch.nn.Dropout(p=dropout)) + self.conv_module.add_module("dropout_3", nn.Dropout(p=dropout)) - self.encoder = torch.nn.Sequential( - self.temporal_frontend, - self.spatial_focus, - self.conv_module + self.encoder = nn.Sequential( + self.temporal_frontend, self.spatial_focus, self.conv_module ) # Shape of intermediate feature maps dense_input_size = self._num_flat_features(input_shape) # DENSE MODULE - self.dense_module = torch.nn.Sequential() + self.dense_module = nn.Sequential() self.dense_module.add_module( "flatten", - torch.nn.Flatten(), + nn.Flatten(), ) self.dense_module.add_module( "fc_out", @@ -227,7 +250,7 @@ def __init__( max_norm=dense_max_norm, ), ) - self.dense_module.add_module("act_out", torch.nn.LogSoftmax(dim=1)) + self.dense_module.add_module("act_out", nn.LogSoftmax(dim=1)) def _num_flat_features(self, input_shape): """Returns the number of flattened features from a tensor. diff --git a/benchmarks/MOABB/models/SpatialFocus.py b/benchmarks/MOABB/models/SpatialFocus.py deleted file mode 100644 index c291e1f34..000000000 --- a/benchmarks/MOABB/models/SpatialFocus.py +++ /dev/null @@ -1,26 +0,0 @@ -import torch -import torch.nn as nn - - -class SpatialFocus(nn.Module): - def __init__(self, projection_dim, position_dim=3, tau=1.0, sigma=0.0): - super().__init__() - self.projection_dim = projection_dim - self.position_dim = position_dim - self.tau = tau - self.sigma = sigma - - self.similarity_module = nn.Sequential( - nn.Linear(position_dim, projection_dim), - nn.ELU(), - nn.Linear(projection_dim, projection_dim), - ) - self.softmax = nn.Softmax(dim=-2) - - def forward(self, x: torch.Tensor, positions: torch.Tensor): - if self.training and self.sigma > 0: - positions = positions + torch.randn_like(positions) * self.sigma - weights = self.similarity_module(positions) - weights = self.softmax(weights / self.tau) - x = torch.einsum("...cf, cd -> ...df", x, weights) - return x From af31aacd0b373b78cc1264dda2d84b8baca860d2 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Mon, 5 Aug 2024 10:47:27 -0400 Subject: [PATCH 055/106] remove position noise to implement later as an augmentation --- .../hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml | 4 +--- benchmarks/MOABB/models/SpatialEEGNet | 5 +---- 2 files changed, 2 insertions(+), 7 deletions(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml index 1c180c1cc..36e0f0c5f 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -37,7 +37,6 @@ tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" n_steps_channel_selection: null T: !apply:math.ceil - !ref * ( - ) -C: 22 # We here specify how to perfom test: # - If test_with: 'last' we perform test with the latest model. # - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) @@ -123,6 +122,7 @@ augment: !new:speechbrain.augment.augmenter.Augmenter max_augmentations: 4 augmentations: [!ref , !ref , !ref , !ref ] +position_noise_sigma: 0 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" # DATA NORMALIZATION dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) @@ -131,7 +131,6 @@ normalize: !name:speechbrain.processing.signal_processing.mean_std_norm # MODEL projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.01, 1.0, precision=4)" -spatial_focus_sigma: 0 # @orion_step1: --spatial_focus_sigma~"uniform(0.0, 0.1, precision=4)" input_shape: [null, !ref , !ref , null] cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" @@ -173,7 +172,6 @@ model: !new:models.SpatialEEGNet.SpatialEEGNet spatial_focus: !new:models.SpatialEEGNet.SpatialFocus projection_dim: !ref position_dim: 3 - sigma: !ref tau: !ref dense_max_norm: !ref dropout: !ref diff --git a/benchmarks/MOABB/models/SpatialEEGNet b/benchmarks/MOABB/models/SpatialEEGNet index 44a5c8359..6c1719b07 100644 --- a/benchmarks/MOABB/models/SpatialEEGNet +++ b/benchmarks/MOABB/models/SpatialEEGNet @@ -12,12 +12,11 @@ import speechbrain as sb class SpatialFocus(nn.Module): - def __init__(self, projection_dim, position_dim=3, tau=1.0, sigma=0.0): + def __init__(self, projection_dim, position_dim=3, tau=1.0): super().__init__() self.projection_dim = projection_dim self.position_dim = position_dim self.tau = tau - self.sigma = sigma self.similarity_module = nn.Sequential( nn.Linear(position_dim, projection_dim), @@ -27,8 +26,6 @@ class SpatialFocus(nn.Module): self.softmax = nn.Softmax(dim=-2) def forward(self, x: torch.Tensor, positions: torch.Tensor): - if self.training and self.sigma > 0: - positions = positions + torch.randn_like(positions) * self.sigma weights = self.similarity_module(positions) weights = self.softmax(weights / self.tau) x = torch.einsum("...cf, cd -> ...df", x, weights) From 5283b699069265e680fcfa52a59dd41c439d9595 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Mon, 5 Aug 2024 11:03:56 -0400 Subject: [PATCH 056/106] fix lazy conversion to graph --- benchmarks/MOABB/utils/dataio_iterators.py | 27 +++++++++++++++------- 1 file changed, 19 insertions(+), 8 deletions(-) diff --git a/benchmarks/MOABB/utils/dataio_iterators.py b/benchmarks/MOABB/utils/dataio_iterators.py index a875f3da2..231a73cad 100644 --- a/benchmarks/MOABB/utils/dataio_iterators.py +++ b/benchmarks/MOABB/utils/dataio_iterators.py @@ -20,13 +20,21 @@ import numpy as np import pandas as pd import torch -from moabb.datasets.base import BaseDataset as MOABBDataset -from torch.utils.data import DataLoader, TensorDataset import torch_geometric.data import torch_geometric.loader +from moabb.datasets.base import BaseDataset as MOABBDataset +from torch.utils.data import DataLoader, IterableDataset, TensorDataset from utils.prepare import prepare_data +class _IterableDatasetFromGenerator(IterableDataset): + def __init__(self, gen): + self.gen = gen + + def __iter__(self): + return iter(self.gen) + + def get_idx_train_valid_classbalanced(idx_train, valid_ratio, y): """This function returns train and valid indices balanced across classes.""" idx_train = np.array(idx_train) @@ -150,6 +158,7 @@ class _SplitDataloaders(TypedDict): ch_names: List[str] ch_positions: np.ndarray adjacency_mtx: np.ndarray + max_T: int XYSplits = namedtuple( @@ -356,6 +365,7 @@ def prepare( ch_names=ch_names, ch_positions=ch_positions, adjacency_mtx=adjacency_mtx, + max_T=max(x_train.shape[1], x_valid.shape[1], x_test.shape[1]), ) tail_path = self.get_tail_path(subject, target_data_dict.get("session")) @@ -453,7 +463,6 @@ def prepare( datasets[key], edge_list=edge_list, ch_positions=torch.from_numpy(datasets["ch_positions"]), - shuffle=(key == "train"), ) return tail_path, datasets @@ -463,20 +472,22 @@ def make_graph_dataloader( dataloader: DataLoader, edge_list: torch.Tensor, ch_positions: torch.Tensor, - shuffle: bool = False, ) -> torch_geometric.loader.DataLoader: - dataset = [ + dataset = _IterableDatasetFromGenerator( # Transpose T, C -> C, T torch_geometric.data.Data( - x=x.transpose(0, 1), y=y, edge_index=edge_list, pos=ch_positions + x=x.transpose(0, 1), + y=y, + edge_index=edge_list, + pos=ch_positions, ) for x, y in dataloader.dataset - ] + ) return torch_geometric.loader.DataLoader( dataset, batch_size=dataloader.batch_size, pin_memory=dataloader.pin_memory, - shuffle=shuffle, + shuffle=False, ) From 3aa906f4ecf3eddcca26a13d9943a90e10a45308 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 15:16:11 -0400 Subject: [PATCH 057/106] torch_geometric implementations of data iterators Supports multiple dataset concatenation --- benchmarks/MOABB/utils/graph_iterators.py | 253 ++++++++++++++++++++++ 1 file changed, 253 insertions(+) create mode 100644 benchmarks/MOABB/utils/graph_iterators.py diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py new file mode 100644 index 000000000..44702c56f --- /dev/null +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -0,0 +1,253 @@ +import abc +import random +from typing import Iterable + +import numpy as np +import pandas as pd +import scipy +import torch +from mne import EpochsArray +from mne.channels import find_ch_adjacency +from moabb import paradigms +from torch.nn import functional as F +from torch.utils.data import ChainDataset, IterableDataset +from torch_geometric.data import Batch, Data +from torch_geometric.loader import DataLoader + + +def rename_subjects(dataset, subjects): + if isinstance(subjects, callable): + dataset.subject_list = subjects(dataset.subject_list) + elif isinstance(subjects, dict): + dataset.subject_list = [subjects[sub] for sub in dataset.subject_list] + elif isinstance(dataset, (list, tuple)): + dataset.subject_list = subjects + + return dataset + + +def _make_paradigm( + dataset, *, fmax, resample=128, fmin=0, tmin=0, tmax=None, **kwargs +): + if tmax is None: + tmax = dataset.interval[1] - dataset.interval[0] + + Paradigm = dict( + imagery=paradigms.MotorImagery, + p300=paradigms.P300, + ssvep=paradigms.SSVEP, + )[dataset.paradigm] + + return Paradigm( + resample=resample, fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax, **kwargs + ) + + +class AsGeometricData(IterableDataset): + X: EpochsArray + Y: np.ndarray + metadata: pd.DataFrame + pos: np.ndarray + adjacency_mtx: np.ndarray + edge_list: np.ndarray + + def __init__( + self, dataset, target_subjects=None, **paradigm_kwargs, + ): + self.dataset = dataset + self.paradigm = _make_paradigm(dataset, **paradigm_kwargs) + self.subjects = target_subjects + + self.is_loaded = False + + def load(self): + self.X, self.Y, self.metadata = self.paradigm.get_data( + self.dataset, subjects=self.subjects, return_epochs=True + ) + + self.pos = list( + self.X.info.get_montage().get_positions()["ch_pos"].values() + ) + self.pos = torch.tensor(self.pos).float().contiguous() + + adjacency_mtx, _ = find_ch_adjacency(self.X.info, ch_type="eeg") + self.adjacency_mtx = scipy.sparse.csr_matrix.toarray( + adjacency_mtx + ) # from sparse mtx to ndarray + self.edge_list = np.vstack(np.nonzero(adjacency_mtx)) + self.edge_list = torch.from_numpy(self.edge_list).float().contiguous() + + self.is_loaded = True + + def __iter__(self): + if not self.is_loaded: + self.load() + + for x, y, (_, m) in zip(self.X, self.Y, self.metadata.iterrows()): + yield Data( + x=torch.from_numpy(x).float(), + y=self.dataset.event_id[y], + edge_index=self.edge_list, + pos=self.pos, + **m, + ) + + +class BaseGraphData(abc.ABC): + train: DataLoader + valid: DataLoader + test: DataLoader + + def __init__( + self, + datasets, + target_subjects, + target_sessions=None, + valid_ratio=0.2, + **paradigm_kwargs, + ): + if not isinstance(datasets, Iterable): + datasets = [datasets] + + self.datasets = datasets + if not isinstance(target_subjects, Iterable): + target_subjects = [target_subjects] + if not isinstance(target_sessions, Iterable): + target_sessions = [target_sessions] + self.target_subjects = target_subjects + self.target_sessions = target_sessions + self.data = ChainDataset(self.make_geometric_data(paradigm_kwargs)) + self.valid_ratio = valid_ratio + + @abc.abstractmethod + def make_geometric_data(self, paradigm_kwargs): + ... + + @abc.abstractmethod + def split_test_data(self, data): + ... + + def data_statistics(self, data): + min_C = np.Inf + max_C = 0 + min_T = np.Inf + max_T = 0 + + for d in data: + C, T, *_ = d.x.shape + min_C = min(min_C, C) + max_C = max(max_C, C) + min_T = min(min_T, T) + max_T = max(max_T, T) + + return min_C, max_C, min_T, max_T + + def pad_data(self, data, max_T): + for d in data: + T = d.x.shape[1] + if T < max_T: + d.x = F.pad(d.x, (0, max_T - T)) + return data + + def prepare(self, batch_size=1, exclude_keys=None): + data = list(self.data) + min_C, max_C, min_T, max_T = self.data_statistics(data) + data = self.pad_data(data, max_T) + train_valid_data, test_data = self.split_test_data(data) + + random.shuffle(train_valid_data) + train_valid_data = Batch.from_data_list(train_valid_data) + + valid_idxs = [] + for c in train_valid_data.y.unique(): + c_idxs = np.nonzero(train_valid_data.y == c) + idx = np.linspace( + 0, + c_idxs.shape[0] - 1, + round(self.valid_ratio * c_idxs.shape[0]), + ).astype(int) + valid_idxs.extend(c_idxs[idx]) + + valid_idxs = np.array(valid_idxs) + train_idxs = np.setdiff1d( + np.arange(len(train_valid_data.y)), valid_idxs + ) + + train = train_valid_data.index_select(train_idxs) + valid = train_valid_data.index_select(valid_idxs) + + if exclude_keys is None: + exclude_keys = [] + exclude_keys += ["subject", "session", "run"] + + self.train = DataLoader( + train, + batch_size=batch_size, + pin_memory=True, + shuffle=True, + exclude_keys=exclude_keys, + ) + self.valid = DataLoader( + valid, + batch_size=batch_size, + pin_memory=True, + exclude_keys=exclude_keys, + ) + self.test = DataLoader( + test_data, + batch_size=batch_size, + pin_memory=True, + exclude_keys=exclude_keys, + ) + + return { + "train": self.train, + "valid": self.valid, + "test": self.test, + "T": max_T, + "C": max_C, + "min_T": min_T, + "min_C": min_C, + } + + +class LeaveOneSessionOut(BaseGraphData): + def make_geometric_data(self, paradigm_kwargs): + return [ + AsGeometricData( + dataset, + # Pass target subject for IO efficiency + target_subjects=list( + set(self.target_subjects).intersection(dataset.subject_list) + ), + **paradigm_kwargs, + ) + for dataset in self.datasets + if set(self.target_subjects).intersection(dataset.subject_list) + ] + + def split_test_data(self, data): + other, test = [], [] + for d in data: + if d.session in self.target_sessions: + test.append(d) + else: + other.append(d) + return other, test + + +class LeaveOneSubjectOut(BaseGraphData): + def make_geometric_data(self, paradigm_kwargs): + return [ + AsGeometricData(dataset, **paradigm_kwargs,) + for dataset in self.datasets + ] + + def split_test_data(self, data): + other, test = [], [] + for d in data: + if d.subject in self.target_subjects: + test.append(d) + else: + other.append(d) + return other, test From 97b08a3384d39124fb7531f2d257f276bd42f58e Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 15:29:27 -0400 Subject: [PATCH 058/106] handle data_folder inputs --- benchmarks/MOABB/utils/graph_iterators.py | 21 +++++++++++++++++++-- 1 file changed, 19 insertions(+), 2 deletions(-) diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py index 44702c56f..510d50e35 100644 --- a/benchmarks/MOABB/utils/graph_iterators.py +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -13,6 +13,7 @@ from torch.utils.data import ChainDataset, IterableDataset from torch_geometric.data import Batch, Data from torch_geometric.loader import DataLoader +from mne.utils.config import set_config, get_config def rename_subjects(dataset, subjects): @@ -101,7 +102,7 @@ class BaseGraphData(abc.ABC): def __init__( self, datasets, - target_subjects, + target_subjects=None, target_sessions=None, valid_ratio=0.2, **paradigm_kwargs, @@ -149,7 +150,23 @@ def pad_data(self, data, max_T): d.x = F.pad(d.x, (0, max_T - T)) return data - def prepare(self, batch_size=1, exclude_keys=None): + def prepare( + self, + data_folder=None, + cached_data_folder=None, + batch_size=1, + exclude_keys=None, + ): + if data_folder is not None: + mne_cfg = get_config() + for a in mne_cfg.keys(): + if ( + mne_cfg[a] != data_folder + ): # reducing writes on mne cfg file to avoid conflicts in parallel trainings + set_config(a, data_folder) + if cached_data_folder is None: + cached_data_folder = data_folder + data = list(self.data) min_C, max_C, min_T, max_T = self.data_statistics(data) data = self.pad_data(data, max_T) From 48c01aa46070837add67ebcae4ee511d0471362c Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 16:04:06 -0400 Subject: [PATCH 059/106] BREAKING: update train py to be compatible with new graphs No longer compatible with upstream --- benchmarks/MOABB/train.py | 121 +++++++++++----------- benchmarks/MOABB/utils/graph_iterators.py | 15 +++ 2 files changed, 75 insertions(+), 61 deletions(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 3617d398a..809d359e2 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -10,6 +10,7 @@ ------ Davide Borra, 2022 Mirco Ravanelli, 2023 +Drew Wagner, 2024 """ import pickle @@ -20,7 +21,7 @@ import numpy as np import logging import sys -from utils.dataio_iterators import LeaveOneSessionOut, LeaveOneSubjectOut +from utils.graph_iterators import LeaveOneSessionOut, LeaveOneSubjectOut from torchinfo import summary import speechbrain as sb import yaml @@ -39,29 +40,28 @@ def init_model(self, model): if mod.bias is not None: init.constant_(mod.bias, 0) - # foo = torch.load('foo.torch') - # model.spatial_focus.similarity_module.load_state_dict(foo) - def compute_forward(self, batch, stage): "Given an input batch it computes the model output." - inputs = batch[0].to(self.device) + inputs = batch.to(self.device) # Perform data augmentation if stage == sb.Stage.TRAIN and hasattr(self.hparams, "augment"): - inputs, _ = self.hparams.augment( - inputs.squeeze(3), - lengths=torch.ones(inputs.shape[0], device=self.device), - ) - inputs = inputs.unsqueeze(3) + inputs.x, _ = self.hparams.augment(inputs.x.transpose(0, 1)) + + if hasattr(self.hparams, "pos_normalize"): + inputs.pos = self.hparams.pos_normalize(inputs.pos) + + if stage == sb.Stage.TRAIN and hasattr(self.hparams, "graph_augment"): + inputs = self.hparams.graph_augment(inputs) # Normalization if hasattr(self.hparams, "normalize"): - inputs = self.hparams.normalize(inputs) - return self.modules.model(inputs, self.hparams.ch_positions) + inputs.x = self.hparams.normalize(inputs.x) + return self.modules.model(inputs) def compute_objectives(self, predictions, batch, stage): "Given the network predictions and targets computes the loss." - targets = batch[1].to(self.device) + targets = batch.y.to(self.device) # Target augmentation N_augments = int(predictions.shape[0] / targets.shape[0]) @@ -86,9 +86,6 @@ def compute_objectives(self, predictions, batch, stage): def on_fit_start(self,): """Gets called at the beginning of ``fit()``""" - self.hparams.ch_positions = self.hparams.ch_positions.float().to( - self.device - ) self.init_model(self.hparams.model) self.init_optimizers() in_shape = ( @@ -191,12 +188,16 @@ def on_stage_end(self, stage, stage_loss, epoch=None): stats_meta={ "epoch loaded": self.hparams.epoch_counter.current }, - test_stats=self.last_eval_stats - if not getattr(self, "log_test_as_valid", False) - else None, - valid_stats=self.last_eval_stats - if getattr(self, "log_test_as_valid", False) - else None, + test_stats=( + self.last_eval_stats + if not getattr(self, "log_test_as_valid", False) + else None + ), + valid_stats=( + self.last_eval_stats + if getattr(self, "log_test_as_valid", False) + else None + ), ) # save the averaged checkpoint at the end of the evaluation stage # delete the rest of the intermediate checkpoints @@ -234,7 +235,7 @@ def check_if_best( self, last_eval_stats, best_eval_stats, keys, ): """Checks if the current model is the best according at least to - one of the monitored metrics. """ + one of the monitored metrics.""" is_best = False for key in keys: if key == "loss": @@ -275,25 +276,15 @@ def run_experiment(hparams, run_opts, datasets): ) logger = logging.getLogger(__name__) logger.info("Experiment directory: {0}".format(hparams["exp_dir"])) - logger.info( - "Input shape: {0}".format( - datasets["train"].dataset.tensors[0].shape[1:] - ) - ) - logger.info( - "Training set avg value: {0}".format( - datasets["train"].dataset.tensors[0].mean() - ) - ) - datasets_summary = "Number of examples: {0} (training), {1} (validation), {2} (test)".format( - datasets["train"].dataset.tensors[0].shape[0], - datasets["valid"].dataset.tensors[0].shape[0], - datasets["test"].dataset.tensors[0].shape[0], - ) - logger.info(datasets_summary) + ch_positions = datasets["ch_positions"] hparams["ch_positions"] = torch.from_numpy( - 2 * ((ch_positions - ch_positions.min(0)) / (ch_positions.max(0) - ch_positions.min(0)) - 0.5) + 2 + * ( + (ch_positions - ch_positions.min(0)) + / (ch_positions.max(0) - ch_positions.min(0)) + - 0.5 + ) ) brain = MOABBBrain( @@ -349,36 +340,44 @@ def prepare_dataset_iterators(hparams): # defining data iterator to use print("Prepare dataset iterators...") if hparams["data_iterator_name"] == "leave-one-session-out": - data_iterator = LeaveOneSessionOut( - seed=hparams["seed"] - ) # within-subject and cross-session + DataIterator = LeaveOneSessionOut elif hparams["data_iterator_name"] == "leave-one-subject-out": - data_iterator = LeaveOneSubjectOut( - seed=hparams["seed"] - ) # cross-subject and cross-session + DataIterator = LeaveOneSubjectOut else: raise ValueError( "Unknown data_iterator_name: %s" % hparams["data_iterator_name"] ) - tail_path, datasets = data_iterator.prepare( - data_folder=hparams["data_folder"], - dataset=hparams["dataset"], - cached_data_folder=hparams["cached_data_folder"], - batch_size=hparams["batch_size"], - valid_ratio=hparams["valid_ratio"], - target_subject_idx=hparams["target_subject_idx"], - target_session_idx=hparams["target_session_idx"], - events_to_load=hparams["events_to_load"], - original_sample_rate=hparams["original_sample_rate"], + data_iterator = DataIterator( + datasets=hparams["datasets"], sample_rate=hparams["sample_rate"], fmin=hparams["fmin"], fmax=hparams["fmax"], tmin=hparams["tmin"], tmax=hparams["tmax"], - save_prepared_dataset=hparams["save_prepared_dataset"], - n_steps_channel_selection=hparams["n_steps_channel_selection"], + events_to_load=hparams["events_to_load"], + valid_ratio=hparams["valid_ratio"], + target_subject_idx=hparams["target_subject_idx"], + target_session_idx=hparams["target_session_idx"], ) + + datasets = data_iterator.prepare( + data_folder=hparams["data_folder"], + cached_data_folder=hparams["cached_data_folder"], + batch_size=hparams["batch_size"], + ) + + tail_path = os.path.join( + hparams["data_iterator_name"], + "_".join( + "sub-{0}".format(str(subject).zfill(3)) + for subject in data_iterator.target_subjects + ), + ) + if data_iterator.target_sessions is not None: + tail_path = os.path.join( + tail_path, "_".join(data_iterator.target_sessions) + ) return tail_path, datasets @@ -392,9 +391,9 @@ def load_hparams_and_dataset_iterators(hparams_file, run_opts, overrides): tail_path, datasets = prepare_dataset_iterators(hparams) # override C and T, to be sure that network input shape matches the dataset (e.g., after time cropping or channel sampling) overrides.update( - T=datasets["train"].dataset.tensors[0].shape[1], - C=datasets["train"].dataset.tensors[0].shape[-2], - n_train_examples=datasets["train"].dataset.tensors[0].shape[0], + T=datasets["T"], + C=datasets["C"], + n_train_examples=len(datasets["train"].dataset), ) # loading hparams for the each training and evaluation processes diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py index 510d50e35..dcb510a13 100644 --- a/benchmarks/MOABB/utils/graph_iterators.py +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -1,3 +1,18 @@ +""" +Graph Data Iterators for MOABB Datasets and Paradigms + +This module provides various data iterators tailored for MOABB datasets and paradigms which +require the electrode position and adjacency data. + +Different training strategies have been implemented as distinct data iterators, including: +- Leave-One-Session-Out +- Leave-One-Subject-Out + +Authors +------ +Drew Wagner, 2024 +""" + import abc import random from typing import Iterable From 93b3f95db0b6f0c2b697b3e6a944eef319b81431 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 16:59:35 -0400 Subject: [PATCH 060/106] Implement position noise + node dropout WARNING: node dropout currently deletes the edge_index! --- benchmarks/MOABB/utils/graph_iterators.py | 33 +++++++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py index dcb510a13..dbff3bd6a 100644 --- a/benchmarks/MOABB/utils/graph_iterators.py +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -20,10 +20,12 @@ import numpy as np import pandas as pd import scipy +from scipy.stats import rv_discrete import torch from mne import EpochsArray from mne.channels import find_ch_adjacency from moabb import paradigms +from torch import nn from torch.nn import functional as F from torch.utils.data import ChainDataset, IterableDataset from torch_geometric.data import Batch, Data @@ -59,6 +61,37 @@ def _make_paradigm( ) +class PositionNoise(nn.Module): + def __init__(self, sigma=1.0): + super().__init__() + self.sigma = sigma + + def forward(self, x): + x.pos = x.pos + torch.randn_like(x.pos) * self.sigma + return x + + +class NodeDrop(nn.Module): + def __init__(self, choose_k): + super().__init__() + self.k = choose_k + + def forward(self, x): + if isinstance(self.k, rv_discrete): + k = self.k.sample() + else: + k = self.k + + del x.edge_index + x = x.to_data_list() + for d in x: + mask = torch.randperm(d.x.shape[0], device=d.x.device)[:k] + d.x = d.x[mask, :] + d.pos = d.pos[mask, :] + + return Batch.from_data_list(x) + + class AsGeometricData(IterableDataset): X: EpochsArray Y: np.ndarray From bc6c12bd0a21370acbac9b777bce7525db4aa327 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:03:14 -0400 Subject: [PATCH 061/106] fix file suffix --- .../MOABB/models/{SpatialEEGNet => SpatialEEGNet.py} | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) rename benchmarks/MOABB/models/{SpatialEEGNet => SpatialEEGNet.py} (97%) diff --git a/benchmarks/MOABB/models/SpatialEEGNet b/benchmarks/MOABB/models/SpatialEEGNet.py similarity index 97% rename from benchmarks/MOABB/models/SpatialEEGNet rename to benchmarks/MOABB/models/SpatialEEGNet.py index 6c1719b07..2b35b783a 100644 --- a/benchmarks/MOABB/models/SpatialEEGNet +++ b/benchmarks/MOABB/models/SpatialEEGNet.py @@ -131,9 +131,7 @@ def __init__( self.temporal_frontend.add_module( "bnorm_0", sb.nnet.normalization.BatchNorm2d( - input_size=cnn_temporal_kernels, - momentum=0.01, - affine=True, + input_size=cnn_temporal_kernels, momentum=0.01, affine=True, ), ) self.spatial_focus = spatial_focus @@ -158,9 +156,7 @@ def __init__( self.conv_module.add_module( "bnorm_1", sb.nnet.normalization.BatchNorm2d( - input_size=cnn_spatial_kernels, - momentum=0.01, - affine=True, + input_size=cnn_spatial_kernels, momentum=0.01, affine=True, ), ) self.conv_module.add_module("act_1", activation) @@ -236,8 +232,7 @@ def __init__( # DENSE MODULE self.dense_module = nn.Sequential() self.dense_module.add_module( - "flatten", - nn.Flatten(), + "flatten", nn.Flatten(), ) self.dense_module.add_module( "fc_out", From 341bb30c236a19e34dba431587bb0ad89cbf47e2 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:15:27 -0400 Subject: [PATCH 062/106] update spatial eegnet --- .../BNCI2014001/SpatialEEGNet.yaml | 18 +++-- benchmarks/MOABB/models/SpatialEEGNet.py | 72 ++++++++++++++----- benchmarks/MOABB/train.py | 7 +- 3 files changed, 72 insertions(+), 25 deletions(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml index 36e0f0c5f..8494eeb73 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -110,8 +110,16 @@ snr_white_high: !ref + add_noise_white: !new:speechbrain.augment.time_domain.AddNoise snr_low: !ref snr_high: !ref +position_noise_sigma: 0 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" repeat_augment: 1 # @orion_step1: --repeat_augment 0 +graph_augment: !new:torch.nn.Sequential + - !new:utils.graph_iterators.PositionNoise + sigma: !ref + - !new:utils.graph_iterators.NodeDrop + choose_k: !new:scipy.stats.uniform + - 4 + - !ref augment: !new:speechbrain.augment.augmenter.Augmenter parallel_augment: True concat_original: True @@ -122,12 +130,13 @@ augment: !new:speechbrain.augment.augmenter.Augmenter max_augmentations: 4 augmentations: [!ref , !ref , !ref , !ref ] -position_noise_sigma: 0 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" # DATA NORMALIZATION dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) normalize: !name:speechbrain.processing.signal_processing.mean_std_norm dims: !ref +pos_normalize: True + # MODEL projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.01, 1.0, precision=4)" @@ -170,10 +179,9 @@ model: !new:models.SpatialEEGNet.SpatialEEGNet cnn_pool_type: !ref activation_type: !ref spatial_focus: !new:models.SpatialEEGNet.SpatialFocus - projection_dim: !ref - position_dim: 3 - tau: !ref + projection_dim: !ref + position_dim: 3 + tau: !ref dense_max_norm: !ref dropout: !ref dense_n_neurons: !ref - diff --git a/benchmarks/MOABB/models/SpatialEEGNet.py b/benchmarks/MOABB/models/SpatialEEGNet.py index 2b35b783a..a4057184d 100644 --- a/benchmarks/MOABB/models/SpatialEEGNet.py +++ b/benchmarks/MOABB/models/SpatialEEGNet.py @@ -1,22 +1,41 @@ -"""EEGNet from https://doi.org/10.1088/1741-2552/aace8c. -Shallow and lightweight convolutional neural network proposed for a general decoding of single-trial EEG signals. -It was proposed for P300, error-related negativity, motor execution, motor imagery decoding. +"""EEGNet adapted to handle variable electrode configurations. + +Base EEGNet implementation borrowed from Davide Borra's implementation in Speechbrain/benchmarks: + + EEGNet from https://doi.org/10.1088/1741-2552/aace8c. + Shallow and lightweight convolutional neural network proposed for a general decoding of single-trial EEG signals. + It was proposed for P300, error-related negativity, motor execution, motor imagery decoding. Authors - * Davide Borra, 2021 + * Drew Wagner, 2024 """ +import speechbrain as sb import torch import torch.nn as nn -import speechbrain as sb +import torch_geometric.utils +from torch_geometric.data import Data + + +class node_wise(nn.Module): + def __init__(self, func): + super().__init__() + self.func = func + + def forward(self, x: Data): + new_x = x.clone() + new_x.x = self.func(x.x.unsqueeze(-1).unsqueeze(-1)).squeeze(-2) + assert new_x.x.shape[:-1] == x.x.shape, "shape changed" + return new_x class SpatialFocus(nn.Module): - def __init__(self, projection_dim, position_dim=3, tau=1.0): + def __init__(self, projection_dim, position_dim=3, tau=1.0, sigma=0.0): super().__init__() self.projection_dim = projection_dim self.position_dim = position_dim self.tau = tau + self.sigma = sigma self.similarity_module = nn.Sequential( nn.Linear(position_dim, projection_dim), @@ -25,14 +44,30 @@ def __init__(self, projection_dim, position_dim=3, tau=1.0): ) self.softmax = nn.Softmax(dim=-2) - def forward(self, x: torch.Tensor, positions: torch.Tensor): + def forward(self, batch: Data): + positions = batch.pos + x = batch.x + + if self.training and self.sigma > 0: + positions = positions + torch.randn_like(positions) * self.sigma weights = self.similarity_module(positions) - weights = self.softmax(weights / self.tau) - x = torch.einsum("...cf, cd -> ...df", x, weights) + weights = ( + torch_geometric.utils.softmax(weights / self.tau, index=batch.batch) + .unsqueeze(1) + .unsqueeze(-1) + ) + x_weighted = x.unsqueeze(-2) * weights + x = torch_geometric.utils.scatter( + x_weighted, + batch.batch, + dim=0, + dim_size=batch.batch_size, + reduce="sum", + ) return x -class SpatialEEGNet(nn.Module): +class EEGNet(nn.Module): """EEGNet. Arguments @@ -134,6 +169,7 @@ def __init__( input_size=cnn_temporal_kernels, momentum=0.01, affine=True, ), ) + self.temporal_frontend = node_wise(self.temporal_frontend) self.spatial_focus = spatial_focus self.conv_module = nn.Sequential() # Spatial depthwise convolution @@ -223,10 +259,6 @@ def __init__( ) self.conv_module.add_module("dropout_3", nn.Dropout(p=dropout)) - self.encoder = nn.Sequential( - self.temporal_frontend, self.spatial_focus, self.conv_module - ) - # Shape of intermediate feature maps dense_input_size = self._num_flat_features(input_shape) # DENSE MODULE @@ -263,16 +295,18 @@ def _num_flat_features(self, input_shape): num_features *= s return num_features - def forward(self, x, positions=None): + def forward(self, x): """Returns the output of the model. Arguments --------- - x : torch.Tensor (batch, time, EEG channel, channel) + x : torch_geometric.Data (batch, time, EEG channel, channel) Input to convolve. 4d tensors are expected. """ - if positions is None: - positions = torch.zeros((x.shape[-2], 3), device=x.device) - x = self.encoder(x) + # x is initially (b*c, t) + x = self.temporal_frontend(x) # returns as Data (b*c, t', d) + x = self.spatial_focus(x) # returns as Tensor (b, t', c', d) + x = self.conv_module(x) # returns as Tensor (b, t'', 1, d') x = self.dense_module(x) + return x diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 809d359e2..75728ddb8 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -49,7 +49,12 @@ def compute_forward(self, batch, stage): inputs.x, _ = self.hparams.augment(inputs.x.transpose(0, 1)) if hasattr(self.hparams, "pos_normalize"): - inputs.pos = self.hparams.pos_normalize(inputs.pos) + if self.hparams.pos_normalize is True: + inputs.pos = (inputs.pos - inputs.pos.min(dim=0)) / ( + inputs.pos.max(dim=0) - inputs.pos.min(dim=0) + ) + elif self.hparams.pos_normalize is not False: + inputs.pos = self.hparams.pos_normalize(inputs.pos) if stage == sb.Stage.TRAIN and hasattr(self.hparams, "graph_augment"): inputs = self.hparams.graph_augment(inputs) From 80aedcd8212d82a3abf621e4c4ce1ea54f570a0b Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:39:17 -0400 Subject: [PATCH 063/106] tmp change idx --- benchmarks/MOABB/train.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 75728ddb8..8e1c6c63f 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -362,8 +362,8 @@ def prepare_dataset_iterators(hparams): tmax=hparams["tmax"], events_to_load=hparams["events_to_load"], valid_ratio=hparams["valid_ratio"], - target_subject_idx=hparams["target_subject_idx"], - target_session_idx=hparams["target_session_idx"], + target_subject=hparams["target_subject_idx"], + target_session=hparams["target_session_idx"], ) datasets = data_iterator.prepare( From 4eb2c8533c942aacd6871bed04d6a9945ff02b9b Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:43:40 -0400 Subject: [PATCH 064/106] don't update C --- benchmarks/MOABB/train.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 8e1c6c63f..5e3d2f98d 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -396,9 +396,7 @@ def load_hparams_and_dataset_iterators(hparams_file, run_opts, overrides): tail_path, datasets = prepare_dataset_iterators(hparams) # override C and T, to be sure that network input shape matches the dataset (e.g., after time cropping or channel sampling) overrides.update( - T=datasets["T"], - C=datasets["C"], - n_train_examples=len(datasets["train"].dataset), + T=datasets["T"], n_train_examples=len(datasets["train"].dataset), ) # loading hparams for the each training and evaluation processes From 96d8f92bcd4077437d7ca017c1fcd55308209f84 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:45:08 -0400 Subject: [PATCH 065/106] add C ref --- .../MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml index 8494eeb73..bca833d4f 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -37,6 +37,7 @@ tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" n_steps_channel_selection: null T: !apply:math.ceil - !ref * ( - ) +C: 22 # We here specify how to perfom test: # - If test_with: 'last' we perform test with the latest model. # - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) From 9465f03e85488d3337219a72f7f13b17e60f0f21 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:45:57 -0400 Subject: [PATCH 066/106] fix dist --- .../MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml index bca833d4f..a7f89d7d0 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -118,7 +118,7 @@ graph_augment: !new:torch.nn.Sequential - !new:utils.graph_iterators.PositionNoise sigma: !ref - !new:utils.graph_iterators.NodeDrop - choose_k: !new:scipy.stats.uniform + choose_k: !apply:scipy.stats.uniform - 4 - !ref augment: !new:speechbrain.augment.augmenter.Augmenter From a3241084dd30bf59a056b955f9dc6369a357c350 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:48:13 -0400 Subject: [PATCH 067/106] fix dist --- .../MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml index a7f89d7d0..44f9fa55b 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -118,7 +118,7 @@ graph_augment: !new:torch.nn.Sequential - !new:utils.graph_iterators.PositionNoise sigma: !ref - !new:utils.graph_iterators.NodeDrop - choose_k: !apply:scipy.stats.uniform + choose_k: !new:scipy.stats._discrete_distns.randint_gen - 4 - !ref augment: !new:speechbrain.augment.augmenter.Augmenter From cb7bb798dc6e9d13e2ff7482239f1330cd6352b9 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:48:56 -0400 Subject: [PATCH 068/106] fix cls name --- benchmarks/MOABB/models/SpatialEEGNet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/models/SpatialEEGNet.py b/benchmarks/MOABB/models/SpatialEEGNet.py index a4057184d..5f220dd7d 100644 --- a/benchmarks/MOABB/models/SpatialEEGNet.py +++ b/benchmarks/MOABB/models/SpatialEEGNet.py @@ -67,7 +67,7 @@ def forward(self, batch: Data): return x -class EEGNet(nn.Module): +class SpatialEEGNet(nn.Module): """EEGNet. Arguments From 556277fe7e6f3f82e3104cad793a7c53ef5b3341 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:52:24 -0400 Subject: [PATCH 069/106] fix node_wise --- benchmarks/MOABB/train.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 5e3d2f98d..8e1c6c63f 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -396,7 +396,9 @@ def load_hparams_and_dataset_iterators(hparams_file, run_opts, overrides): tail_path, datasets = prepare_dataset_iterators(hparams) # override C and T, to be sure that network input shape matches the dataset (e.g., after time cropping or channel sampling) overrides.update( - T=datasets["T"], n_train_examples=len(datasets["train"].dataset), + T=datasets["T"], + C=datasets["C"], + n_train_examples=len(datasets["train"].dataset), ) # loading hparams for the each training and evaluation processes From d313d43d98b901405749b72cff44c31aedbbe9ac Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:54:33 -0400 Subject: [PATCH 070/106] fix node wise --- benchmarks/MOABB/models/SpatialEEGNet.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/benchmarks/MOABB/models/SpatialEEGNet.py b/benchmarks/MOABB/models/SpatialEEGNet.py index 5f220dd7d..415ecbed7 100644 --- a/benchmarks/MOABB/models/SpatialEEGNet.py +++ b/benchmarks/MOABB/models/SpatialEEGNet.py @@ -169,7 +169,6 @@ def __init__( input_size=cnn_temporal_kernels, momentum=0.01, affine=True, ), ) - self.temporal_frontend = node_wise(self.temporal_frontend) self.spatial_focus = spatial_focus self.conv_module = nn.Sequential() # Spatial depthwise convolution @@ -304,7 +303,7 @@ def forward(self, x): Input to convolve. 4d tensors are expected. """ # x is initially (b*c, t) - x = self.temporal_frontend(x) # returns as Data (b*c, t', d) + x = node_wise(self.temporal_frontend)(x) # returns as Data (b*c, t', d) x = self.spatial_focus(x) # returns as Tensor (b, t', c', d) x = self.conv_module(x) # returns as Tensor (b, t'', 1, d') x = self.dense_module(x) From 6cb593932f47c493facad5228a79319ccfd5832c Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:55:38 -0400 Subject: [PATCH 071/106] dataset -> datasets --- .../MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml index 44f9fa55b..ff8cd37b6 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -16,7 +16,8 @@ output_folder: !PLACEHOLDER # Defining the MOABB dataset. -dataset: !new:moabb.datasets.BNCI2014001 +datasets: + - !new:moabb.datasets.BNCI2014001 save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards data_iterator_name: !PLACEHOLDER target_subject_idx: !PLACEHOLDER From 6c6397c9bbea0e709f8c3463362660de670a696a Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:56:58 -0400 Subject: [PATCH 072/106] target sessions / subjects plural --- benchmarks/MOABB/train.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 8e1c6c63f..b8075fa76 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -362,8 +362,8 @@ def prepare_dataset_iterators(hparams): tmax=hparams["tmax"], events_to_load=hparams["events_to_load"], valid_ratio=hparams["valid_ratio"], - target_subject=hparams["target_subject_idx"], - target_session=hparams["target_session_idx"], + target_subjects=hparams["target_subject_idx"], + target_sessions=hparams["target_session_idx"], ) datasets = data_iterator.prepare( From f85d49b5b3909f500df043890e3074725f9d5bda Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:57:57 -0400 Subject: [PATCH 073/106] sample_rate -> resample --- benchmarks/MOABB/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index b8075fa76..b10de6f9c 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -355,7 +355,7 @@ def prepare_dataset_iterators(hparams): data_iterator = DataIterator( datasets=hparams["datasets"], - sample_rate=hparams["sample_rate"], + resample=hparams["sample_rate"], fmin=hparams["fmin"], fmax=hparams["fmax"], tmin=hparams["tmin"], From 106f57a178e02cba030559f9f4dab267ed372f15 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 17:58:38 -0400 Subject: [PATCH 074/106] events_to_load -> events --- benchmarks/MOABB/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index b10de6f9c..18528e96a 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -360,7 +360,7 @@ def prepare_dataset_iterators(hparams): fmax=hparams["fmax"], tmin=hparams["tmin"], tmax=hparams["tmax"], - events_to_load=hparams["events_to_load"], + events=hparams["events_to_load"], valid_ratio=hparams["valid_ratio"], target_subjects=hparams["target_subject_idx"], target_sessions=hparams["target_session_idx"], From 3a6bffb40d665bf12f68c40c8f6a07638cd20b89 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:02:05 -0400 Subject: [PATCH 075/106] fix class weights --- benchmarks/MOABB/train.py | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 18528e96a..dcd97d365 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -13,18 +13,21 @@ Drew Wagner, 2024 """ -import pickle +import logging import os +import pickle +import sys + +import numpy as np +import speechbrain as sb import torch +import yaml from hyperpyyaml import load_hyperpyyaml from torch.nn import init -import numpy as np -import logging -import sys -from utils.graph_iterators import LeaveOneSessionOut, LeaveOneSubjectOut +from torch_geometric.data import Batch from torchinfo import summary -import speechbrain as sb -import yaml + +from utils.graph_iterators import LeaveOneSessionOut, LeaveOneSubjectOut class MOABBBrain(sb.Brain): @@ -258,11 +261,11 @@ def check_if_best( def run_experiment(hparams, run_opts, datasets): """This function performs a single training (e.g., single cross-validation fold)""" - idx_examples = np.arange(datasets["train"].dataset.tensors[0].shape[0]) + ys = Batch.from_data_list(datasets["train"].dataset) + + idx_examples = np.arange(ys.shape[0]) n_examples_perclass = [ - idx_examples[ - np.where(datasets["train"].dataset.tensors[1] == c)[0] - ].shape[0] + idx_examples[np.where(ys == c)[0]].shape[0] for c in range(hparams["n_classes"]) ] n_examples_perclass = np.array(n_examples_perclass) From 560ad9d8300790b7383e4ce48a88b6163dc56915 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:02:40 -0400 Subject: [PATCH 076/106] typo --- benchmarks/MOABB/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index dcd97d365..3524d44ac 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -261,7 +261,7 @@ def check_if_best( def run_experiment(hparams, run_opts, datasets): """This function performs a single training (e.g., single cross-validation fold)""" - ys = Batch.from_data_list(datasets["train"].dataset) + ys = Batch.from_data_list(datasets["train"].dataset).y idx_examples = np.arange(ys.shape[0]) n_examples_perclass = [ From acb4e9a4e1b8b6f6cd8282007098efbb4a852d12 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:04:48 -0400 Subject: [PATCH 077/106] some fixes --- benchmarks/MOABB/train.py | 16 ++++------------ benchmarks/MOABB/utils/graph_iterators.py | 4 ++-- 2 files changed, 6 insertions(+), 14 deletions(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 3524d44ac..77b8c72fb 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -53,8 +53,10 @@ def compute_forward(self, batch, stage): if hasattr(self.hparams, "pos_normalize"): if self.hparams.pos_normalize is True: - inputs.pos = (inputs.pos - inputs.pos.min(dim=0)) / ( - inputs.pos.max(dim=0) - inputs.pos.min(dim=0) + inputs.pos = 2 * ( + (inputs.pos - inputs.pos.min(dim=0)) + / (inputs.pos.max(dim=0) - inputs.pos.min(dim=0)) + - 0.5 ) elif self.hparams.pos_normalize is not False: inputs.pos = self.hparams.pos_normalize(inputs.pos) @@ -285,16 +287,6 @@ def run_experiment(hparams, run_opts, datasets): logger = logging.getLogger(__name__) logger.info("Experiment directory: {0}".format(hparams["exp_dir"])) - ch_positions = datasets["ch_positions"] - hparams["ch_positions"] = torch.from_numpy( - 2 - * ( - (ch_positions - ch_positions.min(0)) - / (ch_positions.max(0) - ch_positions.min(0)) - - 0.5 - ) - ) - brain = MOABBBrain( modules={"model": hparams["model"]}, opt_class=hparams["optimizer"], diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py index dbff3bd6a..e04025047 100644 --- a/benchmarks/MOABB/utils/graph_iterators.py +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -159,9 +159,9 @@ def __init__( datasets = [datasets] self.datasets = datasets - if not isinstance(target_subjects, Iterable): + if not isinstance(target_subjects, (list, tuple)): target_subjects = [target_subjects] - if not isinstance(target_sessions, Iterable): + if not isinstance(target_sessions, (list, tuple)): target_sessions = [target_sessions] self.target_subjects = target_subjects self.target_sessions = target_sessions From 648b468cfc7aab8587aac5ca4c1ede6e74afd9f6 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:06:45 -0400 Subject: [PATCH 078/106] no summary --- benchmarks/MOABB/train.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 77b8c72fb..c555501f5 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -25,7 +25,6 @@ from hyperpyyaml import load_hyperpyyaml from torch.nn import init from torch_geometric.data import Batch -from torchinfo import summary from utils.graph_iterators import LeaveOneSessionOut, LeaveOneSubjectOut @@ -103,13 +102,13 @@ def on_fit_start(self,): + tuple(np.floor(self.hparams.input_shape[1:-1]).astype(int)) + (1,) ) - model_summary = summary( - self.hparams.model, input_size=in_shape, device=self.device - ) - with open( - os.path.join(self.hparams.exp_dir, "model.txt"), "w" - ) as text_file: - text_file.write(str(model_summary)) + # model_summary = summary( + # self.hparams.model, input_size=in_shape, device=self.device + # ) + # with open( + # os.path.join(self.hparams.exp_dir, "model.txt"), "w" + # ) as text_file: + # text_file.write(str(model_summary)) def on_stage_start(self, stage, epoch=None): "Gets called when a stage (either training, validation, test) starts." From cb72ff2f010b0bd0ddf8fc4be503bd20d22054e8 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:09:37 -0400 Subject: [PATCH 079/106] lengths --- benchmarks/MOABB/train.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index c555501f5..78f824998 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -48,7 +48,10 @@ def compute_forward(self, batch, stage): # Perform data augmentation if stage == sb.Stage.TRAIN and hasattr(self.hparams, "augment"): - inputs.x, _ = self.hparams.augment(inputs.x.transpose(0, 1)) + inputs.x, _ = self.hparams.augment( + inputs.x.transpose(0, 1), + lengths=torch.ones(inputs.x.shape[0], device=self.device), + ) if hasattr(self.hparams, "pos_normalize"): if self.hparams.pos_normalize is True: @@ -97,11 +100,11 @@ def on_fit_start(self,): """Gets called at the beginning of ``fit()``""" self.init_model(self.hparams.model) self.init_optimizers() - in_shape = ( - (1,) - + tuple(np.floor(self.hparams.input_shape[1:-1]).astype(int)) - + (1,) - ) + # in_shape = ( + # (1,) + # + tuple(np.floor(self.hparams.input_shape[1:-1]).astype(int)) + # + (1,) + # ) # model_summary = summary( # self.hparams.model, input_size=in_shape, device=self.device # ) From cd9da2f050d1c3600c91f5dbb2657bf05779e972 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:11:00 -0400 Subject: [PATCH 080/106] disable graph augment for testing --- .../MotorImagery/BNCI2014001/SpatialEEGNet.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml index ff8cd37b6..9ded7ca94 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -115,13 +115,13 @@ add_noise_white: !new:speechbrain.augment.time_domain.AddNoise position_noise_sigma: 0 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" repeat_augment: 1 # @orion_step1: --repeat_augment 0 -graph_augment: !new:torch.nn.Sequential - - !new:utils.graph_iterators.PositionNoise - sigma: !ref - - !new:utils.graph_iterators.NodeDrop - choose_k: !new:scipy.stats._discrete_distns.randint_gen - - 4 - - !ref +# graph_augment: !new:torch.nn.Sequential +# - !new:utils.graph_iterators.PositionNoise +# sigma: !ref +# - !new:utils.graph_iterators.NodeDrop +# choose_k: !new:scipy.stats._discrete_distns.randint_gen +# - 4 +# - !ref augment: !new:speechbrain.augment.augmenter.Augmenter parallel_augment: True concat_original: True From 4cef9c8533d8e405b2cffec2b1b451ef1bf6908d Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:12:18 -0400 Subject: [PATCH 081/106] disable augment for testing --- .../BNCI2014001/SpatialEEGNet.yaml | 34 +++++++++---------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml index 9ded7ca94..531ec8ac9 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -115,23 +115,23 @@ add_noise_white: !new:speechbrain.augment.time_domain.AddNoise position_noise_sigma: 0 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" repeat_augment: 1 # @orion_step1: --repeat_augment 0 -# graph_augment: !new:torch.nn.Sequential -# - !new:utils.graph_iterators.PositionNoise -# sigma: !ref -# - !new:utils.graph_iterators.NodeDrop -# choose_k: !new:scipy.stats._discrete_distns.randint_gen -# - 4 -# - !ref -augment: !new:speechbrain.augment.augmenter.Augmenter - parallel_augment: True - concat_original: True - parallel_augment_fixed_bs: True - repeat_augment: !ref - shuffle_augmentations: True - min_augmentations: 4 - max_augmentations: 4 - augmentations: - [!ref , !ref , !ref , !ref ] +graph_augment: !new:torch.nn.Sequential + - !new:utils.graph_iterators.PositionNoise + sigma: !ref + - !new:utils.graph_iterators.NodeDrop + choose_k: !new:scipy.stats._discrete_distns.randint_gen + - 4 + - !ref +# augment: !new:speechbrain.augment.augmenter.Augmenter +# parallel_augment: True +# concat_original: True +# parallel_augment_fixed_bs: True +# repeat_augment: !ref +# shuffle_augmentations: True +# min_augmentations: 4 +# max_augmentations: 4 +# augmentations: +# [!ref , !ref , !ref , !ref ] # DATA NORMALIZATION dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) From 9753be743e6b627fdb7d06378189f841c7949841 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:25:12 -0400 Subject: [PATCH 082/106] fix min max --- benchmarks/MOABB/train.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 78f824998..ab789e122 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -56,8 +56,11 @@ def compute_forward(self, batch, stage): if hasattr(self.hparams, "pos_normalize"): if self.hparams.pos_normalize is True: inputs.pos = 2 * ( - (inputs.pos - inputs.pos.min(dim=0)) - / (inputs.pos.max(dim=0) - inputs.pos.min(dim=0)) + (inputs.pos - inputs.pos.min(dim=0).values) + / ( + inputs.pos.max(dim=0).values + - inputs.pos.min(dim=0).values + ) - 0.5 ) elif self.hparams.pos_normalize is not False: From 5406ac79dab0fb438d50c541a786cb425e749913 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:26:16 -0400 Subject: [PATCH 083/106] typo --- benchmarks/MOABB/utils/graph_iterators.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py index e04025047..53c9af639 100644 --- a/benchmarks/MOABB/utils/graph_iterators.py +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -78,7 +78,7 @@ def __init__(self, choose_k): def forward(self, x): if isinstance(self.k, rv_discrete): - k = self.k.sample() + k = self.k.rvs() else: k = self.k From 2bd46a5d16cde6fab634e622828900b6b1e5202c Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:29:01 -0400 Subject: [PATCH 084/106] apply instead of new --- .../MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml index 531ec8ac9..8515fcc0b 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -119,7 +119,7 @@ graph_augment: !new:torch.nn.Sequential - !new:utils.graph_iterators.PositionNoise sigma: !ref - !new:utils.graph_iterators.NodeDrop - choose_k: !new:scipy.stats._discrete_distns.randint_gen + choose_k: !apply:scipy.stats._discrete_distns.randint_gen - 4 - !ref # augment: !new:speechbrain.augment.augmenter.Augmenter From b494382a93cc4539eff4abce3ad0b82d4a6e9933 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:31:38 -0400 Subject: [PATCH 085/106] no support for discrete dist --- .../hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml | 4 +--- benchmarks/MOABB/utils/graph_iterators.py | 7 +------ 2 files changed, 2 insertions(+), 9 deletions(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml index 8515fcc0b..675ae01ba 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -119,9 +119,7 @@ graph_augment: !new:torch.nn.Sequential - !new:utils.graph_iterators.PositionNoise sigma: !ref - !new:utils.graph_iterators.NodeDrop - choose_k: !apply:scipy.stats._discrete_distns.randint_gen - - 4 - - !ref + choose_k: 12 # augment: !new:speechbrain.augment.augmenter.Augmenter # parallel_augment: True # concat_original: True diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py index 53c9af639..4a00fd21a 100644 --- a/benchmarks/MOABB/utils/graph_iterators.py +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -77,15 +77,10 @@ def __init__(self, choose_k): self.k = choose_k def forward(self, x): - if isinstance(self.k, rv_discrete): - k = self.k.rvs() - else: - k = self.k - del x.edge_index x = x.to_data_list() for d in x: - mask = torch.randperm(d.x.shape[0], device=d.x.device)[:k] + mask = torch.randperm(d.x.shape[0], device=d.x.device)[:self.k] d.x = d.x[mask, :] d.pos = d.pos[mask, :] From 50c6808a2db1c10c2156bb977f815553db44dbe0 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:35:30 -0400 Subject: [PATCH 086/106] fix event id start at 0 --- benchmarks/MOABB/utils/graph_iterators.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py index 4a00fd21a..571a3faed 100644 --- a/benchmarks/MOABB/utils/graph_iterators.py +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -80,7 +80,7 @@ def forward(self, x): del x.edge_index x = x.to_data_list() for d in x: - mask = torch.randperm(d.x.shape[0], device=d.x.device)[:self.k] + mask = torch.randperm(d.x.shape[0], device=d.x.device)[: self.k] d.x = d.x[mask, :] d.pos = d.pos[mask, :] @@ -130,7 +130,7 @@ def __iter__(self): for x, y, (_, m) in zip(self.X, self.Y, self.metadata.iterrows()): yield Data( x=torch.from_numpy(x).float(), - y=self.dataset.event_id[y], + y=self.dataset.event_id[y] - 1, edge_index=self.edge_list, pos=self.pos, **m, From 31285a4a0b9a66fd26d838c4bfc4592b3edebdec Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:38:38 -0400 Subject: [PATCH 087/106] fix target issue --- benchmarks/MOABB/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index ab789e122..c6b50499e 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -93,7 +93,7 @@ def compute_objectives(self, predictions, batch, stage): # From log to linear predictions tmp_preds = torch.exp(predictions) self.preds.extend(tmp_preds.detach().cpu().numpy()) - self.targets.extend(batch[1].detach().cpu().numpy()) + self.targets.extend(batch.y.cpu().numpy()) else: if hasattr(self.hparams, "lr_annealing"): self.hparams.lr_annealing.on_batch_end(self.optimizer) From 5b12fc763d2b5c5f64aecffd8ee0834c7d929051 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 6 Aug 2024 18:41:46 -0400 Subject: [PATCH 088/106] update hparams --- .../MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml index 675ae01ba..92e617c7f 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -112,14 +112,14 @@ snr_white_high: !ref + add_noise_white: !new:speechbrain.augment.time_domain.AddNoise snr_low: !ref snr_high: !ref -position_noise_sigma: 0 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" +position_noise_sigma: 0.01 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" repeat_augment: 1 # @orion_step1: --repeat_augment 0 graph_augment: !new:torch.nn.Sequential - !new:utils.graph_iterators.PositionNoise sigma: !ref - !new:utils.graph_iterators.NodeDrop - choose_k: 12 + choose_k: 17 # augment: !new:speechbrain.augment.augmenter.Augmenter # parallel_augment: True # concat_original: True From b52f550e9de68520a1fca99115bd44e11f157228 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 7 Aug 2024 15:03:31 +0000 Subject: [PATCH 089/106] fix data augmentation --- .../BNCI2014001/SpatialEEGNet.yaml | 20 +++++++++---------- benchmarks/MOABB/train.py | 5 +++-- 2 files changed, 13 insertions(+), 12 deletions(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml index 92e617c7f..72ceb336d 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml @@ -120,16 +120,16 @@ graph_augment: !new:torch.nn.Sequential sigma: !ref - !new:utils.graph_iterators.NodeDrop choose_k: 17 -# augment: !new:speechbrain.augment.augmenter.Augmenter -# parallel_augment: True -# concat_original: True -# parallel_augment_fixed_bs: True -# repeat_augment: !ref -# shuffle_augmentations: True -# min_augmentations: 4 -# max_augmentations: 4 -# augmentations: -# [!ref , !ref , !ref , !ref ] +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] # DATA NORMALIZATION dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index c6b50499e..3c64b1e25 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -48,10 +48,11 @@ def compute_forward(self, batch, stage): # Perform data augmentation if stage == sb.Stage.TRAIN and hasattr(self.hparams, "augment"): - inputs.x, _ = self.hparams.augment( - inputs.x.transpose(0, 1), + aug, _ = self.hparams.augment( + inputs.x.unsqueeze(-1), lengths=torch.ones(inputs.x.shape[0], device=self.device), ) + inputs.x = aug.squeeze(-1) if hasattr(self.hparams, "pos_normalize"): if self.hparams.pos_normalize is True: From 1e2be5b767ab5bcb1c843be14889dbeb8ced5888 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Wed, 7 Aug 2024 16:25:16 +0000 Subject: [PATCH 090/106] index subject + formatting fixes --- benchmarks/MOABB/models/SpatialEEGNet.py | 27 ++++++++++++----------- benchmarks/MOABB/train.py | 6 ++--- benchmarks/MOABB/utils/graph_iterators.py | 4 +++- 3 files changed, 20 insertions(+), 17 deletions(-) diff --git a/benchmarks/MOABB/models/SpatialEEGNet.py b/benchmarks/MOABB/models/SpatialEEGNet.py index 415ecbed7..a1ac0f118 100644 --- a/benchmarks/MOABB/models/SpatialEEGNet.py +++ b/benchmarks/MOABB/models/SpatialEEGNet.py @@ -30,12 +30,11 @@ def forward(self, x: Data): class SpatialFocus(nn.Module): - def __init__(self, projection_dim, position_dim=3, tau=1.0, sigma=0.0): + def __init__(self, projection_dim, position_dim=3, tau=1.0): super().__init__() self.projection_dim = projection_dim self.position_dim = position_dim self.tau = tau - self.sigma = sigma self.similarity_module = nn.Sequential( nn.Linear(position_dim, projection_dim), @@ -48,17 +47,14 @@ def forward(self, batch: Data): positions = batch.pos x = batch.x - if self.training and self.sigma > 0: - positions = positions + torch.randn_like(positions) * self.sigma - weights = self.similarity_module(positions) + weights = self.similarity_module(positions).div_(self.tau) weights = ( - torch_geometric.utils.softmax(weights / self.tau, index=batch.batch) - .unsqueeze(1) - .unsqueeze(-1) + torch_geometric.utils.softmax(weights, index=batch.batch) + .view(weights.shape[0], 1, weights.shape[1], 1) ) - x_weighted = x.unsqueeze(-2) * weights + x = x.unsqueeze(-2) * weights x = torch_geometric.utils.scatter( - x_weighted, + x, batch.batch, dim=0, dim_size=batch.batch_size, @@ -166,7 +162,9 @@ def __init__( self.temporal_frontend.add_module( "bnorm_0", sb.nnet.normalization.BatchNorm2d( - input_size=cnn_temporal_kernels, momentum=0.01, affine=True, + input_size=cnn_temporal_kernels, + momentum=0.01, + affine=True, ), ) self.spatial_focus = spatial_focus @@ -191,7 +189,9 @@ def __init__( self.conv_module.add_module( "bnorm_1", sb.nnet.normalization.BatchNorm2d( - input_size=cnn_spatial_kernels, momentum=0.01, affine=True, + input_size=cnn_spatial_kernels, + momentum=0.01, + affine=True, ), ) self.conv_module.add_module("act_1", activation) @@ -263,7 +263,8 @@ def __init__( # DENSE MODULE self.dense_module = nn.Sequential() self.dense_module.add_module( - "flatten", nn.Flatten(), + "flatten", + nn.Flatten(), ) self.dense_module.add_module( "fc_out", diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 3c64b1e25..acb4093de 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -353,7 +353,7 @@ def prepare_dataset_iterators(hparams): raise ValueError( "Unknown data_iterator_name: %s" % hparams["data_iterator_name"] ) - + data_iterator = DataIterator( datasets=hparams["datasets"], resample=hparams["sample_rate"], @@ -363,7 +363,7 @@ def prepare_dataset_iterators(hparams): tmax=hparams["tmax"], events=hparams["events_to_load"], valid_ratio=hparams["valid_ratio"], - target_subjects=hparams["target_subject_idx"], + target_subjects=hparams["target_subject_idx"] + 1, target_sessions=hparams["target_session_idx"], ) @@ -382,7 +382,7 @@ def prepare_dataset_iterators(hparams): ) if data_iterator.target_sessions is not None: tail_path = os.path.join( - tail_path, "_".join(data_iterator.target_sessions) + tail_path, "_".join(map(str, data_iterator.target_sessions)) ) return tail_path, datasets diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py index 571a3faed..aeff0fa7e 100644 --- a/benchmarks/MOABB/utils/graph_iterators.py +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -288,8 +288,10 @@ def make_geometric_data(self, paradigm_kwargs): def split_test_data(self, data): other, test = [], [] + sessions = set() for d in data: - if d.session in self.target_sessions: + sessions.add(d.session) + if list(sessions).index(d.session) in self.target_sessions: test.append(d) else: other.append(d) From c88151d899a385fb5f0e76c088750dd2a511311e Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Fri, 9 Aug 2024 13:54:31 -0400 Subject: [PATCH 091/106] map to int --- benchmarks/MOABB/utils/graph_iterators.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py index aeff0fa7e..d59c4bcc3 100644 --- a/benchmarks/MOABB/utils/graph_iterators.py +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -20,7 +20,6 @@ import numpy as np import pandas as pd import scipy -from scipy.stats import rv_discrete import torch from mne import EpochsArray from mne.channels import find_ch_adjacency @@ -158,8 +157,8 @@ def __init__( target_subjects = [target_subjects] if not isinstance(target_sessions, (list, tuple)): target_sessions = [target_sessions] - self.target_subjects = target_subjects - self.target_sessions = target_sessions + self.target_subjects = list(map(int, target_subjects)) + self.target_sessions = list(map(int, target_sessions)) self.data = ChainDataset(self.make_geometric_data(paradigm_kwargs)) self.valid_ratio = valid_ratio From 45463cd481239d8d4fa7d3549d57e74b42b73906 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Fri, 9 Aug 2024 18:04:51 +0000 Subject: [PATCH 092/106] fix session issue --- benchmarks/MOABB/multi_eval.sh | 20 ++++++++++++++++ benchmarks/MOABB/run_experiments.sh | 1 + benchmarks/MOABB/train.py | 4 ++-- benchmarks/MOABB/utils/graph_iterators.py | 28 +++++++++++++++++------ 4 files changed, 44 insertions(+), 9 deletions(-) create mode 100755 benchmarks/MOABB/multi_eval.sh diff --git a/benchmarks/MOABB/multi_eval.sh b/benchmarks/MOABB/multi_eval.sh new file mode 100755 index 000000000..a6f7669c0 --- /dev/null +++ b/benchmarks/MOABB/multi_eval.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +# Define the array of numbers +numbers=(17 13 11 9 4) + +set -e +# Iterate over the array +for k in "${numbers[@]}"; do + # Execute the run_experiments.sh script with the specified parameters + ./run_experiments.sh --hparams "hparams/MotorImagery/BNCI2014001/SpatialEEGNet.yaml" \ + --data_folder "~/mne_data" \ + --cached_data_folder "~/mne_data/pkl" \ + --output_folder "results/MotorImagery/BNCI2014001/SpatialEEGNet/eval/choose_k$k" \ + --nsbj 9 \ + --nsess 2 \ + --nruns 10 \ + --train_mode "leave-one-session-out" \ + --channel_drop_choose_k "$k" \ + --device "cuda" +done diff --git a/benchmarks/MOABB/run_experiments.sh b/benchmarks/MOABB/run_experiments.sh index 37af36f9a..1ae135897 100755 --- a/benchmarks/MOABB/run_experiments.sh +++ b/benchmarks/MOABB/run_experiments.sh @@ -225,6 +225,7 @@ for i in $(seq 0 1 $(( nruns - 1 ))); do elif [ "$train_mode" = "leave-one-session-out" ]; then # Loop over sessions + set -e for j in $(seq 0 1 $(( nsess - 1 ))); do run_experiment $j $output_folder_exp done diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index acb4093de..8f1109402 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -380,9 +380,9 @@ def prepare_dataset_iterators(hparams): for subject in data_iterator.target_subjects ), ) - if data_iterator.target_sessions is not None: + if data_iterator.target_session_names is not None: tail_path = os.path.join( - tail_path, "_".join(map(str, data_iterator.target_sessions)) + tail_path, "_".join(map(str, data_iterator.target_session_names)) ) return tail_path, datasets diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py index d59c4bcc3..2461c649c 100644 --- a/benchmarks/MOABB/utils/graph_iterators.py +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -95,7 +95,10 @@ class AsGeometricData(IterableDataset): edge_list: np.ndarray def __init__( - self, dataset, target_subjects=None, **paradigm_kwargs, + self, + dataset, + target_subjects=None, + **paradigm_kwargs, ): self.dataset = dataset self.paradigm = _make_paradigm(dataset, **paradigm_kwargs) @@ -159,16 +162,15 @@ def __init__( target_sessions = [target_sessions] self.target_subjects = list(map(int, target_subjects)) self.target_sessions = list(map(int, target_sessions)) + self.target_session_names = None self.data = ChainDataset(self.make_geometric_data(paradigm_kwargs)) self.valid_ratio = valid_ratio @abc.abstractmethod - def make_geometric_data(self, paradigm_kwargs): - ... + def make_geometric_data(self, paradigm_kwargs): ... @abc.abstractmethod - def split_test_data(self, data): - ... + def split_test_data(self, data): ... def data_statistics(self, data): min_C = np.Inf @@ -213,6 +215,10 @@ def prepare( min_C, max_C, min_T, max_T = self.data_statistics(data) data = self.pad_data(data, max_T) train_valid_data, test_data = self.split_test_data(data) + assert ( + len(train_valid_data) > 0 + ), "Invalid Split: No Training & Validation Data" + assert len(test_data) > 0, "Invalid Split: No Test Data" random.shuffle(train_valid_data) train_valid_data = Batch.from_data_list(train_valid_data) @@ -288,19 +294,27 @@ def make_geometric_data(self, paradigm_kwargs): def split_test_data(self, data): other, test = [], [] sessions = set() + target_sessions = set() for d in data: sessions.add(d.session) - if list(sessions).index(d.session) in self.target_sessions: + sess_idx = sorted(sessions).index(d.session) + if sess_idx in self.target_sessions: + target_sessions.add(d.session) test.append(d) else: other.append(d) + + self.target_session_names = target_sessions return other, test class LeaveOneSubjectOut(BaseGraphData): def make_geometric_data(self, paradigm_kwargs): return [ - AsGeometricData(dataset, **paradigm_kwargs,) + AsGeometricData( + dataset, + **paradigm_kwargs, + ) for dataset in self.datasets ] From 357e26f6d9aca2ba462a94e166784b37b2f389cb Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Sat, 17 Aug 2024 15:49:22 +0000 Subject: [PATCH 093/106] remove assert which is sometimes incorrectly triggered --- benchmarks/MOABB/models/SpatialEEGNet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/MOABB/models/SpatialEEGNet.py b/benchmarks/MOABB/models/SpatialEEGNet.py index a1ac0f118..285d88d38 100644 --- a/benchmarks/MOABB/models/SpatialEEGNet.py +++ b/benchmarks/MOABB/models/SpatialEEGNet.py @@ -25,7 +25,7 @@ def __init__(self, func): def forward(self, x: Data): new_x = x.clone() new_x.x = self.func(x.x.unsqueeze(-1).unsqueeze(-1)).squeeze(-2) - assert new_x.x.shape[:-1] == x.x.shape, "shape changed" + return new_x From b4029f890e046170ce0be17cee80829e7e2692e3 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Mon, 19 Aug 2024 13:47:35 +0000 Subject: [PATCH 094/106] hparams for cross subject --- .../BNCI2014001/SpatialEEGNet_cross.yaml | 187 ++++++++++++++++++ 1 file changed, 187 insertions(+) create mode 100644 benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet_cross.yaml diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet_cross.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet_cross.yaml new file mode 100644 index 000000000..00f7c5874 --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/SpatialEEGNet_cross.yaml @@ -0,0 +1,187 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: + !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder + + +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' + + +output_folder: !PLACEHOLDER + #'path/to/results' + + # DATASET HPARS + # Defining the MOABB dataset. + + +datasets: + - !new:moabb.datasets.BNCI2014001 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 250 # Original sampling rate provided by dataset authors +sample_rate: 125 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.13 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 4 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +n_steps_channel_selection: null +T: !apply:math.ceil + - !ref * ( - ) +C: 22 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: "last" # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: "macro" +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 100 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.01742 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 1 # @orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 15.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref +position_noise_sigma: 0.01 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +graph_augment: !new:torch.nn.Sequential + - !new:utils.graph_iterators.PositionNoise + sigma: !ref + # - !new:utils.graph_iterators.NodeDrop + # choose_k: 17 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref From f74b85179a6abd82b92e475bb72fd59ba2c07b49 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Mon, 19 Aug 2024 13:56:04 +0000 Subject: [PATCH 095/106] hparams 004 --- .../BNCI2014004/SpatialEEGNet_cross.yaml | 187 ++++++++++++++++++ 1 file changed, 187 insertions(+) create mode 100644 benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_cross.yaml diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_cross.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_cross.yaml new file mode 100644 index 000000000..23127895c --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_cross.yaml @@ -0,0 +1,187 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: + !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder + + +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' + + +output_folder: !PLACEHOLDER + #'path/to/results' + + # DATASET HPARS + # Defining the MOABB dataset. + + +datasets: + - !new:moabb.datasets.BNCI2014004 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 250 # Original sampling rate provided by dataset authors +sample_rate: 125 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.13 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 4 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +n_steps_channel_selection: null +T: !apply:math.ceil + - !ref * ( - ) +C: 3 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: "last" # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: "macro" +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 100 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.01742 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 1 # @orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 15.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref +position_noise_sigma: 0.01 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +graph_augment: !new:torch.nn.Sequential + - !new:utils.graph_iterators.PositionNoise + sigma: !ref + # - !new:utils.graph_iterators.NodeDrop + # choose_k: 17 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref From 125e1add57205c6957eddffc0204ae364a089fdb Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Thu, 22 Aug 2024 17:40:48 +0000 Subject: [PATCH 096/106] fix augmentation --- benchmarks/MOABB/train.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/benchmarks/MOABB/train.py b/benchmarks/MOABB/train.py index 8f1109402..fd8ae7438 100644 --- a/benchmarks/MOABB/train.py +++ b/benchmarks/MOABB/train.py @@ -47,12 +47,19 @@ def compute_forward(self, batch, stage): inputs = batch.to(self.device) # Perform data augmentation - if stage == sb.Stage.TRAIN and hasattr(self.hparams, "augment"): + if stage == sb.Stage.TRAIN and hasattr(self.hparams, "augment") and self.hparams.repeat_augment > 0: + # Force concat_original to be false, so we can manually concat the original + self.hparams.augment.concat_original = False aug, _ = self.hparams.augment( inputs.x.unsqueeze(-1), lengths=torch.ones(inputs.x.shape[0], device=self.device), ) - inputs.x = aug.squeeze(-1) + aug = aug.squeeze(-1) + + assert aug.shape == inputs.x.shape, f"Shape changed during augmentation: {aug.shape} != {inputs.x.shape}" + inputs_aug = inputs.clone() + inputs_aug.x = aug.squeeze(-1) + inputs = inputs.concat(inputs_aug) if hasattr(self.hparams, "pos_normalize"): if self.hparams.pos_normalize is True: From 0d2001b5b82c0321fd1e01b40645c1f57d87a3e3 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Sun, 25 Aug 2024 18:20:24 +0000 Subject: [PATCH 097/106] configure caching --- benchmarks/MOABB/utils/graph_iterators.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py index 2461c649c..53f32a85f 100644 --- a/benchmarks/MOABB/utils/graph_iterators.py +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -108,7 +108,12 @@ def __init__( def load(self): self.X, self.Y, self.metadata = self.paradigm.get_data( - self.dataset, subjects=self.subjects, return_epochs=True + self.dataset, + subjects=self.subjects, + return_epochs=True, + cache_config=dict( + save_epochs=True, + use=True), ) self.pos = list( From 10363f4d6a0d94324db956e8ec637a3f46197227 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Mon, 26 Aug 2024 13:43:24 +0000 Subject: [PATCH 098/106] fix git mistake --- benchmarks/MOABB/utils/graph_iterators.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/benchmarks/MOABB/utils/graph_iterators.py b/benchmarks/MOABB/utils/graph_iterators.py index 53f32a85f..7ba29eb07 100644 --- a/benchmarks/MOABB/utils/graph_iterators.py +++ b/benchmarks/MOABB/utils/graph_iterators.py @@ -54,6 +54,9 @@ def _make_paradigm( p300=paradigms.P300, ssvep=paradigms.SSVEP, )[dataset.paradigm] + + if dataset.paradigm == 'p300': + kwargs.pop('events') return Paradigm( resample=resample, fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax, **kwargs From 994dc5d2080a540b1747c7a745d2054b344b7dbf Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Sun, 1 Sep 2024 19:02:24 +0000 Subject: [PATCH 099/106] nb to visual --- .../MOABB/visualize_spatial_focus.ipynb | 5049 +++++++++++++++++ 1 file changed, 5049 insertions(+) create mode 100644 benchmarks/MOABB/visualize_spatial_focus.ipynb diff --git a/benchmarks/MOABB/visualize_spatial_focus.ipynb b/benchmarks/MOABB/visualize_spatial_focus.ipynb new file mode 100644 index 000000000..0153bc7da --- /dev/null +++ b/benchmarks/MOABB/visualize_spatial_focus.ipynb @@ -0,0 +1,5049 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "186f51ac-1412-4028-bbde-a75981f95b40", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/torch/cuda/__init__.py:624: UserWarning: Can't initialize NVML\n", + " warnings.warn(\"Can't initialize NVML\")\n" + ] + } + ], + "source": [ + "from train import load_hparams_and_dataset_iterators\n", + "import torch\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from mne.viz import plot_topomap\n", + "import mne.channels\n", + "import glob\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9acb2196-6810-475f-8e46-4f6f989161b5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-001\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-002\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-003\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-004\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-005\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-006\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-007\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-008\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "Prepare dataset iterators...\n", + "moabb.paradigms.motor_imagery - Choosing from all possible events\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-0train/epo/sub-1_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-1train/epo/sub-1_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-2train/epo/sub-1_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-3test/epo/sub-1_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-1/ses-4test/epo/sub-1_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-1 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-0train/epo/sub-2_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-1train/epo/sub-2_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-2train/epo/sub-2_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-3test/epo/sub-2_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-2/ses-4test/epo/sub-2_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-2 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-0train/epo/sub-3_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-1train/epo/sub-3_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-2train/epo/sub-3_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-3test/epo/sub-3_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-3/ses-4test/epo/sub-3_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-3 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-0train/epo/sub-4_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-1train/epo/sub-4_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-2train/epo/sub-4_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-3test/epo/sub-4_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-4/ses-4test/epo/sub-4_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-4 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-0train/epo/sub-5_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-1train/epo/sub-5_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "140 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-2train/epo/sub-5_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-3test/epo/sub-5_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-5/ses-4test/epo/sub-5_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-5 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-0train/epo/sub-6_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-1train/epo/sub-6_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-2train/epo/sub-6_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-3test/epo/sub-6_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-6/ses-4test/epo/sub-6_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-6 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-0train/epo/sub-7_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-1train/epo/sub-7_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-2train/epo/sub-7_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-3test/epo/sub-7_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-7/ses-4test/epo/sub-7_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-7 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-0train/epo/sub-8_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-1train/epo/sub-8_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-2train/epo/sub-8_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-3test/epo/sub-8_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-8/ses-4test/epo/sub-8_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-8 datatype-epo desc-8545cc1\n", + "moabb.datasets.bids_interface - Attempting to retrieve cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1...\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-0train/epo/sub-9_ses-0train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-1train/epo/sub-9_ses-1train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "120 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-2train/epo/sub-9_ses-2train_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-3test/epo/sub-9_ses-3test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "Reading /home/ubuntu/mne_data/MNE-BIDS-bnci2014-004/sub-9/ses-4test/epo/sub-9_ses-4test_task-imagery_run-0_desc-8545cc1a67224092b185fbe21fd730ec_epo.fif ...\n", + " Found the data of interest:\n", + " t = 3000.00 ... 6896.00 ms\n", + " 0 CTF compensation matrices available\n", + "Not setting metadata\n", + "160 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n", + "moabb.datasets.bids_interface - Finished reading cache of 'BNCI2014-004' sub-9 datatype-epo desc-8545cc1\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Adding metadata with 3 columns\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/pytorch/lib/python3.11/site-packages/moabb/paradigms/base.py:350: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.\n", + " X = mne.concatenate_epochs(X)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 140 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 120 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Loading data for 160 events and 488 original time points ...\n", + "Adding metadata with 3 columns\n", + "6520 matching events found\n", + "No baseline correction applied\n", + "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n", + "-- number of adjacent vertices : 3\n", + "moabb.utils - BNCI2014004 has been renamed to BNCI2014_004. BNCI2014004 will be removed in version 1.1.\n", + "moabb.datasets.base - The dataset class name 'BNCI2014004' must be an abbreviation of its code 'BNCI2014-004'. See moabb.datasets.base.is_abbrev for more information.\n", + "speechbrain.core - Beginning experiment!\n", + "speechbrain.core - Experiment folder: /tmp/foo/leave-one-subject-out/sub-009\n" + ] + } + ], + "source": [ + "subject, session = 0, 0\n", + "weights_array = []\n", + "for subject in range(9):\n", + " hparams, ds = load_hparams_and_dataset_iterators('results/MotorImagery/BNCI2014004/SpatialEEGNet_cross/hopt/best_hparams.yaml', {}, {\n", + " \"data_folder\": \"/home/ubuntu/mne_data\",\n", + " \"output_folder\": \"/tmp/foo\",\n", + " \"cached_data_folder\": \"/home/ubuntu/mne_data/pkl\",\n", + " \"target_subject_idx\": subject,\n", + " \"target_session_idx\": 1,\n", + " \"data_iterator_name\": \"leave-one-subject-out\"\n", + " })\n", + " \n", + " sample = next(iter(ds['train'].dataset))\n", + " \n", + " ckpts = glob.glob(f'results/MotorImagery/BNCI2014004/SpatialEEGNet_cross/hopt/best/BJUZME/*/*/leave-one-subject-out/sub-{str(subject+1).zfill(3)}/save/*/model.ckpt')\n", + " for ckpt in ckpts:\n", + " hparams['model'].load_state_dict(torch.load(ckpt, map_location=torch.device('cpu')))\n", + " ch_positions = sample.pos\n", + " ch_positions_orig = sample.pos\n", + " ch_positions = (\n", + " 2 * ((ch_positions - ch_positions.min(0).values) / (ch_positions.max(0).values - ch_positions.min(0).values) - 0.5)\n", + " )\n", + " weights = hparams['model'].spatial_focus.similarity_module(ch_positions.float()).div_(hparams[\"spatial_focus_tau\"]).softmax(-2)\n", + " weights_array.append(weights)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "23631dc5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.66558677, 0.10161189, 0.23280133],\n", + " [0.5768434 , 0.13060276, 0.29255384],\n", + " [0.13782266, 0.3327731 , 0.5294043 ],\n", + " ...,\n", + " [0.7155922 , 0.08293536, 0.20147242],\n", + " [0.720004 , 0.16219407, 0.11780193],\n", + " [0.48455855, 0.2916509 , 0.22379057]], dtype=float32)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights = torch.hstack(weights_array).T.cpu().detach().numpy()\n", + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "63bb42c0", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import KMeans" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "d0f47e25", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
KMeans(n_clusters=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "KMeans(n_clusters=42)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "km = KMeans(n_clusters=hparams['projection_dim'])\n", + "km.fit(np.log(weights))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "88581334-8c98-4078-a992-7cf19d7f47cf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.039826610100963125\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rows = int(np.ceil(hparams['projection_dim'] / 6))\n", + "fig, axes = plt.subplots(rows, 6, figsize=(2*6, 2*rows), layout=\"constrained\")\n", + "for ax in axes.flat:\n", + " ax.axis('off')\n", + "for i, ax in zip(range(km.cluster_centers_.shape[0]), axes.flat):\n", + " plot_topomap(data=np.exp(km.cluster_centers_[i]), pos=ch_positions_orig[:, [0, 1]], axes=ax, show=False)\n", + "\n", + "plt.savefig(\"bnci2014_004_clusters.pdf\", format=\"PDF\")\n", + "print(km.inertia_ / len(weights))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From c138df4a47906c5ba328ff749e69ebb1f255b5cd Mon Sep 17 00:00:00 2001 From: Jama Hussein Mohamud Date: Tue, 3 Sep 2024 10:55:22 -0400 Subject: [PATCH 100/106] added within-subject yaml configs for 4 datasets --- .../BNCI2014004/SpatialEEGNet_single.yaml | 187 ++++++++++++++++++ .../BNCI2015001/SpatialEEGNet_single.yaml | 176 +++++++++++++++++ .../Lee2019_MI/SpatialEEGNet_single.yaml | 176 +++++++++++++++++ .../BNCI2014009/SpatialEEGNet_single.yaml | 176 +++++++++++++++++ 4 files changed, 715 insertions(+) create mode 100644 benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml create mode 100644 benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml create mode 100644 benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml create mode 100644 benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml new file mode 100644 index 000000000..7a4ae1833 --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml @@ -0,0 +1,187 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: + !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder + + +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' + + +output_folder: !PLACEHOLDER + #'path/to/results' + + # DATASET HPARS + # Defining the MOABB dataset. + + +datasets: + - !new:moabb.datasets.BNCI2014004 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 250 # Original sampling rate provided by dataset authors +sample_rate: 125 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.13 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 4 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +n_steps_channel_selection: null +T: !apply:math.ceil + - !ref * ( - ) +C: 3 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: "last" # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: "macro" +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 100 +lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.01742 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 1 # @orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 15.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref +position_noise_sigma: 0.01 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +graph_augment: !new:torch.nn.Sequential + - !new:utils.graph_iterators.PositionNoise + sigma: !ref + # - !new:utils.graph_iterators.NodeDrop + # choose_k: 17 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml new file mode 100644 index 000000000..b3b2f8450 --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml @@ -0,0 +1,176 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' +output_folder: !PLACEHOLDER #'path/to/results' + +# DATASET HPARS +# Defining the MOABB dataset. +dataset: !new:moabb.datasets.BNCI2015001 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 512 # Original sampling rate provided by dataset authors +sample_rate: 128 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 3.4 # @orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 49.6 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 2 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +# number of steps used when selecting adjacent channels from a seed channel (default at Cz) +n_steps_channel_selection: 3 # @orion_step1: --n_steps_channel_selection~"uniform(1, 3,discrete=True)" +T: !apply:math.ceil + - !ref * ( - ) +C: 13 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: 'last' # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: 'macro' +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 12 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 760 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" +lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 2 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.05772 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 0 # orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 5.4 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 16.8 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: [ + !ref , + !ref , + !ref , + !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref diff --git a/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml new file mode 100644 index 000000000..efa4f0291 --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml @@ -0,0 +1,176 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' +output_folder: !PLACEHOLDER #'path/to/results' + +# DATASET HPARS +# Defining the MOABB dataset. +dataset: !new:moabb.datasets.Lee2019_MI +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 1000 # Original sampling rate provided by dataset authors +sample_rate: 125 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.23 # @orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 21.1 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 2 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 3.2 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +# number of steps used when selecting adjacent channels from a seed channel (default at Cz) +n_steps_channel_selection: 2 # @orion_step1: --n_steps_channel_selection~"uniform(1, 5,discrete=True)" +T: !apply:math.ceil + - !ref * ( - ) +C: 62 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: 'last' # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: 'macro' +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 15 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 821 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" +lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 6 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.3492 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 3 # orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 14.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 7.02 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: [ + !ref , + !ref , + !ref , + !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref diff --git a/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml new file mode 100644 index 000000000..94fa2d2b1 --- /dev/null +++ b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml @@ -0,0 +1,176 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder +cached_data_folder: !PLACEHOLDER #'/path/to/pickled/dataset' +output_folder: !PLACEHOLDER #'path/to/results' + +# DATASET HPARS +# Defining the MOABB dataset. +dataset: !new:moabb.datasets.BNCI2014009 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 256 # Original sampling rate provided by dataset authors +sample_rate: 128 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.12 # @orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 43.3 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 2 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 0.8 +# number of steps used when selecting adjacent channels from a seed channel (default at Cz) +n_steps_channel_selection: 3 # @orion_step1: --n_steps_channel_selection~"uniform(1, 3,discrete=True)" +T: !apply:math.ceil + - !ref * ( - ) +C: 16 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: 'last' # 'last' or 'best' +test_key: "f1" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + pos_label: 1 +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 11 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 894 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" +lr: 0.0001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 6 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 2 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.05772 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 0 # orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 5.4 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 16.8 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: [ + !ref , + !ref , + !ref , + !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref From 9bf094675ff905b88d407e63f550c9a9ac6bc5ac Mon Sep 17 00:00:00 2001 From: Jama Hussein Mohamud Date: Tue, 3 Sep 2024 15:20:03 -0400 Subject: [PATCH 101/106] updated number of epochs for orion search to 500-1000 --- .../hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml | 2 +- .../hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml | 2 +- .../hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml | 2 +- .../MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml index 7a4ae1833..23127895c 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml @@ -60,7 +60,7 @@ metrics: n_train_examples: 100 # it will be replaced in the train script # checkpoints to average avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 100 +number_of_epochs: 100 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml index b3b2f8450..bb9b51998 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml @@ -52,7 +52,7 @@ metrics: n_train_examples: 100 # it will be replaced in the train script # checkpoints to average avg_models: 12 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 760 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" +number_of_epochs: 760 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) diff --git a/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml index efa4f0291..2bfa9ca22 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml @@ -52,7 +52,7 @@ metrics: n_train_examples: 100 # it will be replaced in the train script # checkpoints to average avg_models: 15 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 821 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" +number_of_epochs: 821 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) diff --git a/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml index 94fa2d2b1..9e19f5d5e 100644 --- a/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml +++ b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml @@ -52,7 +52,7 @@ metrics: n_train_examples: 100 # it will be replaced in the train script # checkpoints to average avg_models: 11 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 894 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" +number_of_epochs: 894 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" lr: 0.0001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) From 8e84c6fcfaea890af0b3bfb5412b47f9cf4e896c Mon Sep 17 00:00:00 2001 From: Jama Hussein Mohamud Date: Tue, 3 Sep 2024 20:59:25 -0400 Subject: [PATCH 102/106] corected number epochs in the orion flag --- .../hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml | 2 +- .../hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml | 2 +- .../hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml | 2 +- .../MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml index 23127895c..9d3988c63 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml @@ -60,7 +60,7 @@ metrics: n_train_examples: 100 # it will be replaced in the train script # checkpoints to average avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 100 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +number_of_epochs: 100 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True) lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml index bb9b51998..9670e14e3 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml @@ -52,7 +52,7 @@ metrics: n_train_examples: 100 # it will be replaced in the train script # checkpoints to average avg_models: 12 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 760 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +number_of_epochs: 760 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True) lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) diff --git a/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml index 2bfa9ca22..9282e37c0 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml @@ -52,7 +52,7 @@ metrics: n_train_examples: 100 # it will be replaced in the train script # checkpoints to average avg_models: 15 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 821 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +number_of_epochs: 821 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True) lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) diff --git a/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml index 9e19f5d5e..cfb42e235 100644 --- a/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml +++ b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml @@ -52,7 +52,7 @@ metrics: n_train_examples: 100 # it will be replaced in the train script # checkpoints to average avg_models: 11 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 894 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +number_of_epochs: 894 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True) lr: 0.0001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) From 4f0a6dabdd7e8af7886feeae0db6dfc8b1ebcffb Mon Sep 17 00:00:00 2001 From: Jama Hussein Mohamud Date: Tue, 3 Sep 2024 21:45:02 -0400 Subject: [PATCH 103/106] fix bug in the config and comment confusion matrix --- .../MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml | 4 ++-- .../MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml | 4 ++-- .../hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml | 4 ++-- .../MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml | 4 ++-- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml index 9d3988c63..df4516fba 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_single.yaml @@ -55,12 +55,12 @@ cm: !name:sklearn.metrics.confusion_matrix metrics: f1: !ref acc: !ref - cm: !ref + # cm: !ref # TRAINING HPARS n_train_examples: 100 # it will be replaced in the train script # checkpoints to average avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 100 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True) +number_of_epochs: 100 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml index 9670e14e3..04211aff9 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_single.yaml @@ -47,12 +47,12 @@ cm: !name:sklearn.metrics.confusion_matrix metrics: f1: !ref acc: !ref - cm: !ref + # cm: !ref # TRAINING HPARS n_train_examples: 100 # it will be replaced in the train script # checkpoints to average avg_models: 12 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 760 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True) +number_of_epochs: 760 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) diff --git a/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml index 9282e37c0..3a54a955e 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/SpatialEEGNet_single.yaml @@ -47,12 +47,12 @@ cm: !name:sklearn.metrics.confusion_matrix metrics: f1: !ref acc: !ref - cm: !ref + # cm: !ref # TRAINING HPARS n_train_examples: 100 # it will be replaced in the train script # checkpoints to average avg_models: 15 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 821 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True) +number_of_epochs: 821 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) diff --git a/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml index cfb42e235..5e70ed446 100644 --- a/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml +++ b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_single.yaml @@ -47,12 +47,12 @@ cm: !name:sklearn.metrics.confusion_matrix metrics: f1: !ref acc: !ref - cm: !ref + # cm: !ref # TRAINING HPARS n_train_examples: 100 # it will be replaced in the train script # checkpoints to average avg_models: 11 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" -number_of_epochs: 894 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True) +number_of_epochs: 894 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)" lr: 0.0001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" # Learning rate scheduling (cyclic learning rate is used here) max_lr: !ref # Upper bound of the cycle (max value of the lr) From 6abe1fdf8008e93d31a39b40cc5a881075d839b7 Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 15 Oct 2024 15:19:30 +0000 Subject: [PATCH 104/106] hparams 009 --- .../P300/BNCI2014009/SpatialEEGNet_cross.yaml | 187 ++++++++++++++++++ 1 file changed, 187 insertions(+) create mode 100644 benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_cross.yaml diff --git a/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_cross.yaml b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_cross.yaml new file mode 100644 index 000000000..8a3843ba5 --- /dev/null +++ b/benchmarks/MOABB/hparams/P300/BNCI2014009/SpatialEEGNet_cross.yaml @@ -0,0 +1,187 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: + !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder + + +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' + + +output_folder: !PLACEHOLDER + #'path/to/results' + + # DATASET HPARS + # Defining the MOABB dataset. + + +datasets: + - !new:moabb.datasets.BNCI2014009 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 256 # Original sampling rate provided by dataset authors +sample_rate: 128 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 0.12 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 2 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 0.8 +n_steps_channel_selection: null +T: !apply:math.ceil + - !ref * ( - ) +C: 16 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: "last" # 'last' or 'best' +test_key: "f1" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: "macro" +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 100 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.01742 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 1 # @orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 15.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref +position_noise_sigma: 0.01 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +graph_augment: !new:torch.nn.Sequential + - !new:utils.graph_iterators.PositionNoise + sigma: !ref + # - !new:utils.graph_iterators.NodeDrop + # choose_k: 17 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref From a3bd28a4e64dd3bf2b274139daa62857b8615d02 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Tue, 15 Oct 2024 15:31:21 +0000 Subject: [PATCH 105/106] hparams 2015001 --- .../BNCI2015001/SpatialEEGNet_cross.yaml | 187 ++++++++++++++++++ 1 file changed, 187 insertions(+) create mode 100644 benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_cross.yaml diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_cross.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_cross.yaml new file mode 100644 index 000000000..5dc44ada2 --- /dev/null +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/SpatialEEGNet_cross.yaml @@ -0,0 +1,187 @@ +seed: 1234 +__set_torchseed: !apply:torch.manual_seed [!ref ] + +# DIRECTORIES +data_folder: + !PLACEHOLDER #'/path/to/dataset'. The dataset will be automatically downloaded in this folder + + +cached_data_folder: !PLACEHOLDER #'path/to/pickled/dataset' + + +output_folder: !PLACEHOLDER + #'path/to/results' + + # DATASET HPARS + # Defining the MOABB dataset. + + +datasets: + - !new:moabb.datasets.BNCI2015001 +save_prepared_dataset: True # set to True if you want to save the prepared dataset as a pkl file to load and use afterwards +data_iterator_name: !PLACEHOLDER +target_subject_idx: !PLACEHOLDER +target_session_idx: !PLACEHOLDER +events_to_load: null # all events will be loaded +original_sample_rate: 512 # Original sampling rate provided by dataset authors +sample_rate: 128 # Target sampling rate (Hz) +# band-pass filtering cut-off frequencies +fmin: 3.4 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" +n_classes: 2 +# tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class +# trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long +# dataset interval starts from 2 +# -->tmin tmax are referred to this start value (e.g., tmin=0.5 corresponds to 2.5 s) +tmin: 0. +tmax: 4.0 # @orion_step1: --tmax~"uniform(1.0, 4.0, precision=2)" +n_steps_channel_selection: null +T: !apply:math.ceil + - !ref * ( - ) +C: 13 +# We here specify how to perfom test: +# - If test_with: 'last' we perform test with the latest model. +# - if test_with: 'best, we perform test with the best model (according to the metric specified in test_key) +# The variable avg_models can be used to average the parameters of the last (or best) N saved models before testing. +# This can have a regularization effect. If avg_models: 1, the last (or best) model is used directly. +test_with: "last" # 'last' or 'best' +test_key: "acc" # Possible opts: "loss", "f1", "auc", "acc" + +# METRICS +f1: !name:sklearn.metrics.f1_score + average: "macro" +acc: !name:sklearn.metrics.balanced_accuracy_score +cm: !name:sklearn.metrics.confusion_matrix +metrics: + f1: !ref + acc: !ref + cm: !ref +# TRAINING HPARS +n_train_examples: 100 # it will be replaced in the train script +# checkpoints to average +avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)" +number_of_epochs: 100 # orion_step1: --number_of_epochs~"uniform(500, 1000, discrete=True)" +lr: 0.0001 # orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])" +# Learning rate scheduling (cyclic learning rate is used here) +max_lr: !ref # Upper bound of the cycle (max value of the lr) +base_lr: 0.00000001 # Lower bound in the cycle (min value of the lr) +step_size_multiplier: 5 #from 2 to 8 +step_size: !apply:round + - !ref * / +lr_annealing: !new:speechbrain.nnet.schedulers.CyclicLRScheduler + base_lr: !ref + max_lr: !ref + step_size: !ref +label_smoothing: 0.0 +loss: !name:speechbrain.nnet.losses.nll_loss + label_smoothing: !ref +optimizer: !name:torch.optim.Adam + lr: !ref +epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter # epoch counter + limit: !ref +batch_size_exponent: 4 # @orion_step1: --batch_size_exponent~"uniform(4, 6,discrete=True)" +batch_size: !ref 2 ** +valid_ratio: 0.2 + +# DATA AUGMENTATION +# cutcat (disabled when min_num_segments=max_num_segments=1) +max_num_segments: 3 # @orion_step2: --max_num_segments~"uniform(2, 6, discrete=True)" +cutcat: !new:speechbrain.augment.time_domain.CutCat + min_num_segments: 2 + max_num_segments: !ref +# random amplitude gain between 0.5-1.5 uV (disabled when amp_delta=0.) +amp_delta: 0.01742 # @orion_step2: --amp_delta~"uniform(0.0, 0.5)" +rand_amp: !new:speechbrain.augment.time_domain.RandAmp + amp_low: !ref 1 - + amp_high: !ref 1 + +# random shifts between -300 ms to 300 ms (disabled when shift_delta=0.) +shift_delta_: 1 # @orion_step2: --shift_delta_~"uniform(0, 25, discrete=True)" +shift_delta: !ref 1e-2 * # 0.250 # 0.-0.25 with steps of 0.01 +min_shift: !apply:math.floor + - !ref 0 - * +max_shift: !apply:math.floor + - !ref 0 + * +time_shift: !new:speechbrain.augment.freq_domain.RandomShift + min_shift: !ref + max_shift: !ref + dim: 1 +# injection of gaussian white noise +snr_white_low: 15.0 # @orion_step2: --snr_white_low~"uniform(0.0, 15, precision=2)" +snr_white_delta: 19.1 # @orion_step2: --snr_white_delta~"uniform(5.0, 20.0, precision=3)" +snr_white_high: !ref + +add_noise_white: !new:speechbrain.augment.time_domain.AddNoise + snr_low: !ref + snr_high: !ref +position_noise_sigma: 0.01 # @orion_step1: --position_noise_sigma~"uniform(0.0, 0.1, precision=4)" + +repeat_augment: 1 # @orion_step1: --repeat_augment 0 +graph_augment: !new:torch.nn.Sequential + - !new:utils.graph_iterators.PositionNoise + sigma: !ref + # - !new:utils.graph_iterators.NodeDrop + # choose_k: 17 +augment: !new:speechbrain.augment.augmenter.Augmenter + parallel_augment: True + concat_original: True + parallel_augment_fixed_bs: True + repeat_augment: !ref + shuffle_augmentations: True + min_augmentations: 4 + max_augmentations: 4 + augmentations: + [!ref , !ref , !ref , !ref ] + +# DATA NORMALIZATION +dims_to_normalize: 1 # 1 (time) or 2 (EEG channels) +normalize: !name:speechbrain.processing.signal_processing.mean_std_norm + dims: !ref +pos_normalize: True + +# MODEL +projection_dim: 22 # @orion_step1: --projection_dim~"uniform(4, 44, discrete=True)" +spatial_focus_tau: 0.3333 # @orion_step1: --spatial_focus_tau~"uniform(0.001, 1.0, precision=4)" + +input_shape: [null, !ref , !ref , null] +cnn_temporal_kernels: 61 # @orion_step1: --cnn_temporal_kernels~"uniform(32, 72,discrete=True)" +cnn_temporal_kernelsize: 51 # @orion_step1: --cnn_temporal_kernelsize~"uniform(24, 62,discrete=True)" +# depth multiplier for the spatial depthwise conv. layer +cnn_spatial_depth_multiplier: 4 # @orion_step1: --cnn_spatial_depth_multiplier~"uniform(1, 4,discrete=True)" +cnn_spatial_max_norm: 1. # kernel max-norm constaint of the spatial depthwise conv. layer +cnn_spatial_pool: 4 +cnn_septemporal_depth_multiplier: 1 # depth multiplier for the separable temporal conv. layer +cnn_septemporal_point_kernels_ratio_: 7 # @orion_step1: --cnn_septemporal_point_kernels_ratio_~"uniform(0, 8, discrete=True)" +cnn_septemporal_point_kernels_ratio: !ref / 4 +## number of temporal filters in the separable temporal conv. layer +cnn_septemporal_point_kernels_: !ref * * +cnn_septemporal_point_kernels: !apply:math.ceil + - !ref * + 1 +cnn_septemporal_kernelsize_: 15 # @orion_step1: --cnn_septemporal_kernelsize_~"uniform(3, 24,discrete=True)" +max_cnn_spatial_pool: 4 +cnn_septemporal_kernelsize: !apply:round + - !ref * / +cnn_septemporal_pool: 7 # @orion_step1: --cnn_septemporal_pool~"uniform(1, 8,discrete=True)" +cnn_pool_type: "avg" +dense_max_norm: 0.25 # kernel max-norm constaint of the dense layer +dropout: 0.008464 # @orion_step1: --dropout~"uniform(0.0, 0.5)" +activation_type: "elu" + +model: !new:models.SpatialEEGNet.SpatialEEGNet + input_shape: !ref + cnn_temporal_kernels: !ref + cnn_temporal_kernelsize: [!ref , 1] + cnn_spatial_depth_multiplier: !ref + cnn_spatial_max_norm: !ref + cnn_spatial_pool: [!ref , 1] + cnn_septemporal_depth_multiplier: !ref + cnn_septemporal_point_kernels: !ref + cnn_septemporal_kernelsize: [!ref , 1] + cnn_septemporal_pool: [!ref , 1] + cnn_pool_type: !ref + activation_type: !ref + spatial_focus: !new:models.SpatialEEGNet.SpatialFocus + projection_dim: !ref + position_dim: 3 + tau: !ref + dense_max_norm: !ref + dropout: !ref + dense_n_neurons: !ref From 505d21b5e4e8ddf0745c95e6b72c654f4d4041cf Mon Sep 17 00:00:00 2001 From: Drew Wagner Date: Tue, 15 Oct 2024 15:36:15 +0000 Subject: [PATCH 106/106] hparams 2014004 --- .../hparams/MotorImagery/BNCI2014004/SpatialEEGNet_cross.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_cross.yaml b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_cross.yaml index 23127895c..c8c465a80 100644 --- a/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_cross.yaml +++ b/benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/SpatialEEGNet_cross.yaml @@ -26,9 +26,9 @@ events_to_load: null # all events will be loaded original_sample_rate: 250 # Original sampling rate provided by dataset authors sample_rate: 125 # Target sampling rate (Hz) # band-pass filtering cut-off frequencies -fmin: 0.13 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" +fmin: 0.97 # orion_step1: --fmin~"uniform(0.1, 5, precision=2)" fmax: 46.0 # @orion_step1: --fmax~"uniform(20.0, 50.0, precision=3)" -n_classes: 4 +n_classes: 2 # tmin, tmax respect to stimulus onset that define the interval attribute of the dataset class # trial begins (0 s), cue (2 s, 1.25 s long); each trial is 6 s long # dataset interval starts from 2