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Update ept_calculateClusters.m #31

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35 changes: 20 additions & 15 deletions ResultViewer/Dependencies/ept_calculateClusters.m
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Original file line number Diff line number Diff line change
Expand Up @@ -13,12 +13,12 @@
% Look for new clusters
x = TFCE_Obs; % copy TFCE_Obs
x(P_Values> threshold) = 0; % Threshold the data at alpha
Cp = ept_ClusRes(x, ChN, 0.01); % Calculate Positive Clusters
Cp = ept_ClusRes(x, ChN, 0.05); % Calculate Positive Clusters

xn = x; % copy x
xn(x>0)=0; % Only show negative x
xn = abs(xn); % Make negative x's positive
Cn = ept_ClusRes(xn, ChN, 0.01); % Calculate Negative Clusters
Cn = ept_ClusRes(xn, ChN, 0.05); % Calculate Negative Clusters

all_clusters = Cn-Cp; % Combine the two

Expand Down Expand Up @@ -74,7 +74,7 @@
% Calculate clusters above p-value threshold
data = TFCE_Obs; % copy TFCE_Obs
data(P_Values>threshold) = 0; % Threshold the data at alpha
all_clusters = ept_ClusRes3D(data, ChN, 0.01); % Calculate Negative Clusters
all_clusters = ept_ClusRes3D(data, ChN, 0.05); % Calculate Negative Clusters

unique_clusters = unique(all_clusters); % How many different clusters are there?
unique_clusters(unique_clusters==0)=[]; % Eliminate the 0 from being a unique cluster
Expand All @@ -93,17 +93,22 @@
sig_ind = find(all_clusters== unique_clusters(n)); % find the rows and columns that are significant
[SizeC, SizeF, sig_samples]=ind2sub(size(all_clusters),sig_ind);

ClusRes{n,1} = peak_channel(1); % peak channel (just the first of many possible peak channels (but averaging may result in a channel in between two that is not significant)!
ClusRes{n,2} = peak_sample(1);
ClusRes{n,3} = PeakF(1);
ClusRes{n,4} = Obs_Values(peak_channel(1),PeakF(1),peak_sample(1));
ClusRes{n,5} = P_Values(peak_channel(1),PeakF(1),peak_sample(1));
ClusRes{n,6} = numel(sig_ind);
ClusRes{n,7} = numel(unique(SizeC));
ClusRes{n,8} = numel(unique(sig_samples));
ClusRes{n,9} = numel(unique(SizeF));
ClusRes{n,10} = [num2str(min(sig_samples)), ' - ', num2str(max(sig_samples))];
ClusRes{n,11} = [num2str(min(SizeF)), ' - ', num2str(max(SizeF))];
cluster_results(n).channel_peak = peak_channel(1); % peak channel (just the first of many possible peak channels (but averaging may result in a channel in between two that is not significant)!
cluster_results(n).sample_peak = peak_sample(1);
cluster_results(n).freq_peak = PeakF(1);
cluster_results(n).max_t_value = Obs_Values(peak_channel(1),PeakF(1),peak_sample(1));
cluster_results(n).p_value_peak = P_Values(peak_channel(1),PeakF(1),peak_sample(1));
cluster_results(n).cluster_size = numel(sig_ind);
cluster_results(n).unique_channels = numel(unique(SizeC));
cluster_results(n).unique_samples = numel(unique(sig_samples));
cluster_results(n).unique_freqs = numel(unique(SizeF));
cluster_results(n).sample_range = [num2str(min(sig_samples)), ' - ', num2str(max(sig_samples))];
cluster_results(n).freq_range = [num2str(min(SizeF)), ' - ', num2str(max(SizeF))];

% export the actual supra-cluster points
cluster_results(n).cluster_locations = false(size(Results.P_Values));
cluster_results(n).cluster_locations(...
all_clusters == unique_clusters(n)) = true;

end
end
end