Overview

This script uses MARA (Multiple Artifact Rejection Algorithm) which automates the process of identifying components that are artifacts. It is a supervised machine learning technique which was trained to identify artifact components from expert ratings, thus it is reliable. It seems to handle 'eye-artifacts, muscle artifacts and loose electrodes equally' Mara plug-ing. Additionally, that same link contains a MARA Tutorial .pdf which explains MARA more in depth. Lastly, it is good to just review information for ICA and component rejection from the EEGLAB Wiki.

The main line code from this script is:

[ALLEEG,EEG,CURRENTSET] = processMARA(ALLEEG,EEG,CURRENTSET)

Part 1: Load in EEG Raw File Names2.xlsx

• Loading in Sheet 1 is what makes this script clean only eyes-closed EEG files.

% Set the working directory
cd('Y:\STUDY 1\All EEG Files Organized\Preprocessed_RAW')

% Specify the filename
filename = 'EEG Raw File Names2.xlsx';

% Read the data from the Excel file (Eyes-Closed)
data = readtable(filename, 'Sheet', 1);

Part 2: Set the input and save pathways

% 1. Set the folder path to record the names of all the files there
% that need ICA run
input_filepath = 'Y:\STUDY 1\All EEG Files Organized\Preprocessed_RAW\RAW_eyes_open_and_eyes_closed_after_ICA\'; % replace with the path to your folder

% 2. Set the folder path that you want the EEG data saved in
save_pathway = 'Y:\STUDY 1\All EEG Files Organized\Preprocessed_RAW\RAW_eyes_open_and_eyes_closed_after_component_rejection\';

% 3. Set the folder path that you want the CSV reports to be saved
save_pathway_csv = 'Y:\STUDY 1\All EEG Files Organized\Preprocessed_RAW\CSV_components rejected\';

Part 3: Remove file names that have already been preprocessed

% % % % % % Part 1: Reading in all the files in specified folder % % % % %
% % % % % % REMAINING CODE IS AUTOMATIC % % % % % % % % 
fileNames = data.file_name2;

% Remove redundancies
% Only unprocessed files will be ran by the for loop below
Already_Processed= dir(save_pathway);

% Extract .set files
Already_Processed = {Already_Processed(contains({Already_Processed.name}, ".set")).name};

% Remove files that have already been preprocessed
eegFiles = fileNames(~ismember(fileNames,Already_Processed));

Part 4: Using MARA to reject artifact components

% % % % % % Part 1: Reading in all the files in specified folder % % % % %
% % % % % % REMAINING CODE IS AUTOMATIC % % % % % % % % 
fileNames = data.file_name2;

% Remove redundancies
% Only unprocessed files will be ran by the for loop below
Already_Processed= dir(save_pathway);

% Extract .set files
Already_Processed = {Already_Processed(contains({Already_Processed.name}, ".set")).name};

% Remove files that have already been preprocessed
eegFiles = fileNames(~ismember(fileNames,Already_Processed));


% % % Part 2: Running the cleaning script on the following files % % % %
for ii = 1:length(eegFiles)

    Current_eegFile = eegFiles{ii} %MUST BE SQUIGGLY LINE FOR SEGMENTATION TO WORK!!!!
        
    %Import data - change the name of the ID
    EEG = pop_loadset('filename',Current_eegFile,'filepath',input_filepath);
    [ALLEEG, EEG, CURRENTSET] = eeg_store( ALLEEG, EEG, 0 );

    % Run MARA
    [ALLEEG,EEG,CURRENTSET] = processMARA(ALLEEG,EEG,CURRENTSET)
    EEG = eeg_checkset( EEG );

    % Record number of components flagged for rejection
    Rejected_Component = find(EEG.reject.gcompreject == 1);
    RejectedComponentNum = length(Rejected_Component);
    
    % Reject the flagged components
    EEG = pop_subcomp(EEG, Rejected_Component, 0);
    
    % Save output into DataLog    
    DataLog = table( ...
        {Current_eegFile},...
        RejectedComponentNum', ...
        'VariableNames', { ...
        'FileName',...
        'CompRejNum'});

    % This sets the working directory where the CSV files will be saved
    cd(save_pathway_csv);
    
    % Save the file
    writetable(DataLog, [Current_eegFile '.csv']);
    
    % Saving the EEG data
    Save_FileName = Current_eegFile
    EEG = pop_saveset(EEG, 'filename',Save_FileName, ...
        'filepath',[save_pathway]);

end