Wavelet packet coefficients weighted for frequency optimization in motor imagery based brain-computer interfaces

Common spatial patterns (CSP) is a successful algorithm in motor imagery (MI) based brain-computer interfaces (BCI). However, the performance of the algorithm in electroencephalography (EEG) classification depends largely on the subject´s specific frequency band. In this paper, we proposed a new wavelet packet coefficients weighted CSP (WPW-CSP) algorithm to optimize the frequency band of the specific subject for motor imagery (MI) classification. The algorithm is applied to 5 MI data sets of two classes. The results suggest that the proposed algorithm achieves a 4% increase of classification accuracy in off-line analysis, compared to the original CSP algorithm using 8-30 Hz wide band.