Error entropy based adaptive kernel classification for non-stationary EEG analysis

The performance of Brain-Computer Interface (BCI) applications are sometimes hindered by non-stationarity in the EEG data from sessions on different days. This paper proposes an algorithm for adaptive training of a SVM classifier to address the non-stationarity in EEG by adapting the kernel to data from subsequent sessions. The kernel width parameter of the kernel function of the SVM classifier is adapted using an information theoretic cost function based on minimum error entropy (MEE). An experiment is performed using the proposed method on EEG data collected without feedback from 12 healthy subjects in two sessions on separate days. The results using the proposed method yielded a mean accuracy of 75%, which is significantly better compared to the baseline result of 67% without kernel adaptation (P=0.00029).