Classification of yoga, meditation, combined yoga-meditation EEG signals using L-SVM, KNN, and MLP classifiers

In this study, we compare the classification accuracy achievable with linear support vector machine (L-SVM), K-nearest neighbor (KNN), and multilayer perceptron (MLP) methods for a multi-class EEG signal. This can be done in three phases. In phase one, band-pass filtering is applied to raw electroencephalogram (EEG) signals to decompose into five different frequency subbands. In phase two, we extract 10 important features from each subband. In phase three, these extracted feature sets are used as input to L-SVM, KNN, and MLP classifiers which categorize the sample data into three classes namely yoga, meditation, and combined yoga-meditation. Various performance measures for each classifier are evaluated and then compared to know which classifier is effective in the classification of the EEG data into yoga, meditation, and combined yoga-meditation groups. Performance measures such as confusion matrix, accuracy, sensitivity, specificity, precision, and F1 score are used to validate the performance of classifiers. Kruskal-Wallis test has been conducted to compare the classification performance of the linear SVM, KNN, and MLP classifier models. By comparing the classification accuracy between the three classifiers, L-SVM achieved the highest accuracy of 91.67%.