AVI_SSVEP_classification.m

%% Classification of SSVEP signals on AVI SSVEP dataset using decision tree

fs = 512;
windowTimes = [1 2 3 4 5];
freqRanges = [.05 .1 .2 .3 .4];

figure()
t = tiledlayout(5, 5, TileSpacing="compact", Padding="compact");
% For every window length
for windowTime = windowTimes
    % For every frequancy range
    for freqRange = freqRanges
        nexttile
        
        % Create [X,Y] from my dataset
        [X1,Y1] = createDataset(6, windowTime, freqRange, fs);
        [X2,Y2] = createDataset(7.4, windowTime, freqRange, fs);

        % Search for the best threshold on my dataset
        bestThreshold6 = find_best_threshold(X1, Y1, 1);
        bestThreshold7_4 = find_best_threshold(X2, Y2, 1);

        % Create [X_test,Y_test] on AVI SSVEP dataset
        [X1_test,Y1_test] = createAVIDataset(6, windowTime, freqRange, fs);
        [X2_test,Y2_test] = createAVIDataset(7.5, windowTime, freqRange, fs);
        X_test = cat(2, X1_test, X2_test); % input matrix: maximum values of fft around targetFreqs
        Y_test = Y1_test + (2*Y2_test); % labels: 1=6Hz, 2=7.4Hz

        % Classify the test data
        y_test_pred = SSVEP_classifier(X_test, bestThreshold6, bestThreshold7_4);
        
        % Compute confusion matrix
        C = confusionmat(Y_test, y_test_pred, "Order", [1 2 0]);
        cm = confusionchart(C,["6 Hz" "7.5 Hz" "null"]);
        cm.RowSummary = 'row-normalized';
        cm.Title = sprintf("WindowTime = %ds, freqRange = %.2fHz", windowTime, freqRange);
    end
end
title(t, "SSVEP classification on AVI dataset")