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function D = ImaGIN_BadChannel(S)
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% Extract badchannels indices
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% Detect bad channels indices with a trained classifier.
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%
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% -=============================================================================
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% This function is part of the ImaGIN software:
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% https://f-tract.eu/
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...
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@@ -16,62 +17,96 @@ function D = ImaGIN_BadChannel(S) |
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%
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% Authors: Viateur Tuyisenge & Olivier David
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try
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FileIn = S.dataset; % name of cropped iEEG file .dat/.mat
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%badDir = S.DirFileOut; % directory where iEEG file .dat/.mat with badchannels indices will be stored
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trainDir = S.trainBase; % directory where training set is stored
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try
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D = spm_eeg_load(FileIn); % Load the cropped meeg object
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catch
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FileIn = spm_select(1, '\.mat$', 'Select data file');
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D=spm_eeg_load(FileIn);
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% Name of input iEEG file .dat/.mat (cropped)
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if (nargin >= 1) && isfield(S, 'dataset') && ~isempty(S.dataset)
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FileIn = S.dataset;
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else
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FileIn = spm_select(1, '\.mat$', 'Select EEG mat file');
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if isempty(FileIn)
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return;
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end
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end
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% Directory where iEEG file .dat/.mat with badchannels indices will be stored
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if (nargin >= 1) && isfield(S, 'FileOut') && ~isempty(S.FileOut)
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[badDir, FileOut] = fileparts(S.FileOut);
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isNewFile = 1;
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else
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[badDir, FileOut] = fileparts(FileIn);
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isNewFile = 0;
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end
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% Directory where training set is stored (if not defined, use the toolbox folder)
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if (nargin >= 1) && isfield(S, 'trainBase') && ~isempty(S.trainBase)
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trainDir = S.trainBase;
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else
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trainDir = fileparts(mfilename('fullpath'));
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end
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% Detect bad channels and save summary images
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try
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% Load the cropped meeg object
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D = spm_eeg_load(FileIn);
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[badDir,FileOut,~] = fileparts(S.FileOut);
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bPrefix = strcat(badDir,'/',FileOut);
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% Compute signal features (apply artifact correction only for stims)
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clear S2;
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S2.FileName = FileIn;
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if ~isempty(D.events) && ~isempty(D.events.type) && ismember('Stim', {D.events.type})
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S2.InterpolationFilter = 1;
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else
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S2.InterpolationFilter = 0;
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end
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T = ImaGIN_FeatureSEEG(S2);
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% T = ImaGIN_FeatureSEEG(S2); % function returns a table T of features
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% If the trained classifier is not available: compute it
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trainedFile = fullfile(trainDir, 'ImaGIN_trainedClassifier.mat');
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if ~exist(trainedFile, 'file')
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% Get train base file
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trainBaseFile = fullfile(trainDir, 'ImaGIN_trainBaseFeatures.mat');
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if ~exist(trainBaseFile, 'file')
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error(['File ' trainBaseFile ' was not found.']);
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end
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% Load the train base
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trainBase = load(trainBaseFile);
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% Train the classifier
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trainedClassifier = ImaGIN_trainClassifier(trainBase.predictors, trainBase.response);
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% Otherwise, load the trained classifier
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else
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trainedClassifier = load(trainedFile);
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end
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% Tbase = readtable(strcat(trainDir, '/trainBaseFeatures.csv')); % load training base
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% trainingData = Tbase(:,2:9);
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% [trainedClassifier, ~] = ImaGIN_trainClassifier(trainingData); % train the model
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%Uncomment the following line to use the trained model
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% load(strcat(trainDir, '/ImaGIN_trainedClassifier.mat')) % load the trained classifier
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% channelClass = trainedClassifier.predictFcn(T(:,2:8)); % predict new dataset
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% bd = strcmp(channelClass','Bad);
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bd = strcmp('channelClass','Bad');
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% Predict new dataset
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channelClass = trainedClassifier.predictFcn(T(:,2:8));
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% Get list of detected bad channels
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bIdx = find(strcmp(channelClass, 'Bad'));
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bIdx = find(bd);
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badFile = fopen(strcat(bPrefix,'_bIdx.txt'),'w'); % Save badchannel indices in .txt file
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fprintf(badFile,'%d\n',bIdx(:));
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% Save bad channel indices in .txt file
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badFile = fopen(fullfile(badDir, [FileOut, '_bIdx.txt']), 'w');
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fprintf(badFile, '%d\n', bIdx(:));
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fclose(badFile);
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% Tnew = [T channelClass];
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% Tnew.Properties.VariableNames{'Var9'} = 'Note';
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% csvfilename = strcat(bPrefix,'.csv'); % Save feature table & badchannels indices
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% writetable(Tnew,csvfilename,'Delimiter',',');
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% Add badchannel index in meeg object
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if ~isempty(bIdx)
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D = badchannels(D,bIdx,1); %Add badchannel index in meeg object
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D = badchannels(D,bIdx,1);
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end
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% Create new file in output (otherwise, simply update the input file)
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if isNewFile
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Dbad = clone(D, fullfile(badDir, FileOut), [D.nchannels D.nsamples D.ntrials]); % save meeg with badchannel indices in Badchannel directory
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Dbad(:,:,:) = D(:,:,:);
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end
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Dbad = clone(D, bPrefix, [D.nchannels D.nsamples D.ntrials]); % save meeg with badchannel indices in Badchannel directory
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Dbad(:,:,:) = D(:,:,:);
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% Save modified list of bad channels
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save(Dbad);
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%Channel plots and ScreenShots
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figDir = strcat(badDir, '/ScreenShot');
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% Channel plots and ScreenShots
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figDir = fullfile(badDir, 'ScreenShot');
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if ~exist(figDir, 'dir')
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mkdir(figDir);
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end
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close all;
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Size = 8; %Number of channels per screenshot
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Size = 8; % Number of channels per screenshot
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n_c = size(D,1);
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if n_c >= Size
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tmp = floor(n_c/Size);
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...
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...
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@@ -148,8 +183,9 @@ try |
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close
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end
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set_final_status('OK')
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catch ME
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fprintf('%s \n',ME.message)
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fprintf('%s \n', ME.message)
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set_final_status('NOK')
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end
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...
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...
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