Optimal Spatio-spectral Variable Size Subbands Filter for Motor Imagery Brain Computer Interface

Common spatial pattern (CSP) is a commonly used feature extraction technique for motor imagery brain computer interface. CSP provides poor performance when features are extracted from unfiltered or irrelevant frequency band filtered data. In order to overcome this problem, Subband CSP (SBCSP) and Filter Bank CSP (FBCSP) have been proposed in literature to extract features from several fixed size subbands. However, both SBCSP and FBCSP require manually fixing the size of subbands to obtain higher performance. In this paper, we propose a method that obtains features from many variable size subbands within a given frequency band using CSP. Further, Euclidean distance measure is used to obtain the relevant features. The efficacy of the proposed method is evaluated in terms of classification error on BCI Competition III dataset IVa and BCI competition IV dataset Ia. Experimental results demonstrate that the proposed method achieves better performance in comparison to CSP, SBCSP and FBCSP.