إخراج النتائج للورقة والتحليل الإحصائي

[ALLEEG, EEG, CURRENTSET, ALLCOM] = eeglab;
% Folder for the course data (change it)
dataFolder = 'C:\Users\me\Documents\eeg-course';

% Download the ready Go-nogo STUDY once and unzip it in its own folder
zipDir = fullfile(dataFolder, 'animal_study');
studyFile = dir(fullfile(zipDir, '**', 'animal.study'));
if isempty(studyFile)
    zipFile = fullfile(dataFolder, 'animal_study.zip');
    websave(zipFile, 'https://sccn.ucsd.edu/eeglab/download/animal_study.zip');
    unzip(zipFile, zipDir);
    studyFile = dir(fullfile(zipDir, '**', 'animal.study'));
end
studyDir = studyFile(1).folder;

% Convert the old .dat data files to .fdt once: resaving them in their old format damages the data in EEGLAB 2026.1
setFiles = dir(fullfile(studyDir, '*', '*.set'));
for k = 1:numel(setFiles)
    [~, base] = fileparts(setFiles(k).name);
    folder = setFiles(k).folder;
    if ~exist(fullfile(folder, [base '.fdt']), 'file')
        TMP = pop_loadset('filename', setFiles(k).name, 'filepath', folder);
        pop_saveset(TMP, 'filename', setFiles(k).name, 'filepath', folder, 'savemode', 'twofiles');
        if exist(fullfile(folder, [base '.fdt']), 'file') && exist(fullfile(folder, [base '.dat']), 'file')
            delete(fullfile(folder, [base '.dat']));
        end
    end
end

% Rebuild animal_mine.study (create-study lesson) if it is missing
if ~exist(fullfile(studyDir, 'animal_mine.study'), 'file')
    gender = containers.Map( ...
        {'cba','clm','ega','fsa','gro','hth','lmi','mba','mma','mta','pla','sce','sph','wpa'}, ...
        {'F',  'F',  'F',  'M',  'M',  'M',  'F',  'F',  'M',  'F',  'M',  'F',  'M',  'M'});
    files = dir(fullfile(studyDir, '*', '*.set'));
    commands = {};
    for k = 1:numel(files)
        hdr = pop_loadset('filename', files(k).name, 'filepath', files(k).folder, 'loadmode', 'info');
        subj = hdr.subject;
        if isempty(subj), [~, subj] = fileparts(files(k).folder); end
        cond = hdr.condition;
        if isempty(cond)
            if contains(files(k).name, 'distractor'), cond = 'distractor'; else, cond = 'animal'; end
        end
        if ~isKey(gender, lower(subj))
            error('No gender entry for subject %s (%s)', subj, files(k).name);
        end
        commands{end+1} = {'index', k, 'load', fullfile(files(k).folder, files(k).name), ...
            'subject', subj, 'condition', cond, 'group', gender(lower(subj))};
    end
    [STUDY, ALLEEG] = std_editset([], [], 'name', 'Go-nogo animal (mine)', ...
        'task', 'Go-nogo categorization', 'commands', commands, 'updatedat', 'on');
    STUDY = pop_savestudy(STUDY, ALLEEG, 'filename', 'animal_mine.study', ...
        'filepath', studyDir, 'resavedatasets', 'on');
end
[STUDY, ALLEEG] = pop_loadstudy('filename', 'animal_mine.study', 'filepath', studyDir);

% Designs 2 and 3 (study-design lesson); design 2 is the current one
STUDY = std_makedesign(STUDY, ALLEEG, 2, 'name', 'image x gender', ...
    'variable1', 'condition', 'values1', {'animal', 'distractor'}, 'vartype1', 'categorical', ...
    'variable2', 'group', 'values2', {'F', 'M'}, 'vartype2', 'categorical');
STUDY = std_makedesign(STUDY, ALLEEG, 3, 'name', 'image only', ...
    'variable1', 'condition', 'values1', {'animal', 'distractor'}, 'vartype1', 'categorical', ...
    'variable2', 'none');
STUDY = std_selectdesign(STUDY, ALLEEG, 2);
STUDY = pop_savestudy(STUDY, ALLEEG, 'savemode', 'resave');

% Precomputed measures (study-precompute lesson), only if they are missing
first = STUDY.datasetinfo(1);
timefFile = fullfile(first.filepath, [first.subject '.dattimef']);
needMeasures = ~exist(fullfile(first.filepath, [first.subject '.daterp']), 'file') || ~exist(timefFile, 'file');
if ~needMeasures
    tf = load('-mat', timefFile, 'labels');
    needMeasures = numel(tf.labels) < ALLEEG(1).nbchan;   % ERSP from an older two-channel run
end
if needMeasures
    baseline = [round(ALLEEG(1).xmin*1000) 0];
    [STUDY, ALLEEG] = std_precomp(STUDY, ALLEEG, {}, 'savetrials', 'on', 'interp', 'on', 'recompute', 'on', ...
        'erp', 'on', 'erpparams', {'rmbase', baseline}, ...
        'spec', 'on', 'specparams', {'specmode', 'fft', 'logtrials', 'off'}, ...
        'ersp', 'on', 'itc', 'on', 'erspparams', {'cycles', [3 0.8], 'freqs', [3 30], 'nfreqs', 20, 'ntimesout', 50});
    STUDY = pop_savestudy(STUDY, ALLEEG, 'savemode', 'resave');
end

CURRENTSTUDY = 1; EEG = ALLEEG; CURRENTSET = 1:length(EEG);
eeglab redraw;