Local temporal common spatial patterns modulated with phase locking value

Abstract In the field of electroencephalogram (EEG)-based brain–computer interfaces (BCI), the method of common spatial patterns (CSP) is formulated as a problem of eigen-decomposition of covariance matrices. By using temporally local samples to construct the covariance matrices, the approach of local temporal common spatial patterns (LTCSP) are developed, which performs manifold modeling. It is useful to consider the intrinsic structure of samples in defining the temporally local covariance matrices. From the perspective of neurophysiological knowledge, phase synchronization indicates information communication. In this paper, we apply phase locking value (PLV) to quantify the phase relationship between samples, which are then adopted as weights to define the temporally local covariance matrices. As a result, we obtain the PLV-modulated LTCSP. More discriminative features are discovered with the approach proposed. Experiments of EEG classification on three EEG data sets (i.e., the data sets IIIa and IVa of BCI competition III and the data set IIa of BCI competition IV) demonstrate the effectiveness of the proposed technique. The average classification accuracies of the proposed method on the three data sets are 90.56%, 83.25%, and 83.26%. In the case of noise introduced, the average classification accuracy of the proposed method exceeds the conventional CSP and LTCSP by nearly 10% and 6%, respectively. The hypothesis test indicates the superiority of the proposed method in terms of statistical significance.