Band selection by criterion of common spatial patterns for motor imagery based brain machine interfaces

Design of spatial weights and filters that extract components related to brain activity of motor imagery is a crucial issue in brain machine interfaces. This paper proposes a novel method to design these filters. We use a similarity of the covariance matrices of narrow band observed signals over frequency bins. The similarity is defined based on the common spatial pattern method. This proposed method enables us to design the multiple bandpass filters. The experimental results of classification of EEG signals during motor imagery show that the proposed method achieves higher classification accuracy than well-known conventional methods.