Maximum Variance-based EEG Time Bin Selection for Decoding of Imagined Hand Movement Directions in Brain Computer Interface

Abstract

Motor-Imagery-based Brain Computer Interface (MI-BCI) decodes the parameters of imagined motor movement and translates it into control commands to the external world. It has potential applications in neurorehabilitation and development of assistive technology. This paper investigates the Electroencephalogram (EEG) correlates of direction parameters of a center-out hand movement imagination task in right and left directions. A variance-based time bin selection algorithm is proposed to select the most discriminative EEG time segment for directional classification of movement imagination. The discriminative EEG features carrying motor imagery (MI) directional information are extracted from the selected EEG time segment using the wavelet-common spatial pattern (WCSP) algorithm. The WCSP features are classified using Support Vector Machine classifier resulting in a cross validated classification accuracy of 71% between left versus right MI directions of 15 subjects.