Analytic Common Spatial Pattern and Adaptive Classification for Multiclass Motor Imagery-based BCI

This paper focuses on the classification of motor imagery tasks from electroencephalogram (EEG) for brain computer interfaces (BCI). A new processing algorithm based on filter bank common spatial pattern (FBCSP) is presented. Analytic common spatial pattern (ACSP) and adaptive classification are introduced to investigate whether they can improve the performance. Four versions of FBCSP, namely, common spatial pattern (CSP) and ACSP with static or adaptive classification are studied. The session-to-session performances of the proposed approaches are evaluated on a 4-class problem posed in the BCI Competition IV dataset 2a. Our results demonstrate the effectiveness of the proposed methods in comparison to the winner of the BCI Competition IV Dataset 2a as well as other more recent studies using this dataset. Adaptive classification yields a higher kappa value of 0.61 compared to 0.57 for multiclass FBCSP algorithm. ACSP further improves the performance achieving a mean kappa of 0.63.