Classification of EEG Signals for a Hypnotrack BCI System

Abstract

People’s responses to a hypnosis intervention is diverse and unpredictable. A system that predicts user’s level of susceptibility from their electroencephalography (EEG) signals can be helpful in clinical hypnotherapy sessions. In this paper, we extracted differential entropy (DE) of the recorded EEGs from two groups of subjects with high and low hypnotic susceptibility and built a support vector machine on these DE features for the classification of susceptibility trait. Moreover, we proposed a clustering-based feature refinement strategy to improve the estimation of such trait. Results showed a high classification performance in detection of subjects’ level of susceptibility before and during hypnosis. Our results suggest the usefulness of this classifier in development of future BCI systems applied in the domain of therapy and healthcare.