MuseTetris

This repository contains code to run an adaptation of Tetris that dynamically changes the game speed based on Machine Learning predictions of the user's current Arousal and Valence. This project was originally built to run an experiment called EEGoFeedback: Dynamic Flow Maintenance through Real-time EEG Emotion Prediction.

Repo Structure:

.
├── dreamer_model
|   ├── datasets  # all generated csv go here
|   ├── ML  # contains models, utils, and training split functions
|   ├── features.ipynb  # generates features tables from DREAMER
|   ├── predictor_model.ipynb   # train model to predict in-game
|   └── run_models.ipynb    # notebook to compile all models
├── experiment
|   ├── EpocX   # contains code to get and manipulate data from EEG
|   ├── Muse   # contains code to get and manipulate data from EEG
|   ├── experiment_muse.py   # functions for syncing EEG data with game
└── tetris
    └── main.py

Initial Setup

Once you have set up your python environment, run

pip install -r requirements.txt

Playing the game/Data Collection

Current tetris implementation only tested with Python 3.13.7

To play the game, set the current working directory to Tetris-EEG/tetris, then run main.py. Make sure that a Muse EEG headset is close by and powered on.

First run:

muselsl stream

After streaming is up and running, run:

python -m tetris

Controls

Button	Action	Description
Left Arrow	Move Left	Moves the current piece to the left
Right Arrow	Move Right	Moves the current piece to the right
Up Arrow	Rotate Piece	Rotates the current piece
Down Arrow	Soft Drop	Increases fall speed of the current piece
Space	Hard Drop	Instantly lands and locks current piece
C	Hold/Swap	Stashes current piece in the Hold cell, swaps with piece in hold cell if available