A High Performance Approach for Classification of Motor Imagery EEG

Use of Motor Imagery in EEG signals is gaining importance to develop Brain Computer Interface (BCI) applications in various fields ranging from bio-medical to entertainment and its classification at lower complexity in EEG signals is gaining importance. Filter Bank Common Spatial Pattern (FBCSP) has been a promising feature extraction approach to deal with subject-specific behavior in Motor Imagery classification. Though its computational complexity has been on the lower side, the approach has not yet matched the classification accuracy of non-FBCSP approaches. By introducing a combined feature set of Band Power (BP) features and Time Domain (TDP) features in FBCSP approach we have succeeded in proposing an approach of high classification accuracy while out competing the computational complexity of the best reported non-FBCSP approaches. We have also analyzed the impact of parameter variations on classification accuracy and achieved 0.59 mean kappa value for Dataset 2a BCI competition IV on four class problem. This is the highest reported for FBCSP approaches along with the lowest inter-subject variation.