Common spatial patterns combined with phase synchronization information for classification of EEG signals

Abstract The common spatial patterns (CSP) approach is a classical and representative technique of optimizing spatial filters of electroencephalogram (EEG) signals in the community of brain computer interfaces (BCI). It, however, utilizes only amplitude information of the EEG signals. The phase information of the multi-channel EEG series, on the other hand, plays an important role in characterizing brain activities. In this paper, we consider enhancing the classification performance of CSP by making explicit use of information of the phase synchronization. An index, termed as rank-weighted phase lag index (rWPLI), is introduced to qualify the intrinsic phase synchronization. The rWPLI features are then incorporated into the CSP framework via three ways of feature combinations. The classification experiments on real EEG data sets of BCI competitions show the effectiveness of the proposed framework.