A critical comparison between SVM and k-SVM in the classification of Kriya Yoga meditation state-allied EEG

Abstract

Support vector machines (SVM) have become a gold standard method for the classification of brain signals. However, for highly nonlinear and non-stationary signals like Electroencephalography (EEG), conventional SVM is not sufficient to classify the different brain states associated with different cognitive activity. Brain state classification is a challenging task when using standard SVM. Thus, a Kernel-based SVM (k-SVM) has been undertaken in the present study for classification between non-meditation (controlled group) and meditation based EEG. The k-SVM is popularly known as a non-linear classifier. In the present work, a comparative study has been taken up to classify the resting brain state associated with Kriya Yoga meditation practice using SVM and Kernel-SVM (k-SVM). The EEG signals have been captured from ten non-meditators (control group) and 23 meditators group. The results of both SVM and k-SVM have been shown and compared in both the groups. Additionally, the average classification accuracy has been found to be 85.543% for SVM and 90.8259% for k-SVM. The obtained results show that the kernel-based SVM surpassed the conventional SVM in classifying the meditation and non-meditation allied EEG.