Phase synchronization analysis of EEG channels using bivariate empirical mode decomposition

The paper presents a novel concept implementing a phase locking value index estimation in application to brain-computer interfacing (BCI) motor imagery paradigm. We propose to decompose first the pairs of EEG channels using a bivariate empirical mode decomposition (BEMD) method. Next, the phase locking values (PLV) are estimated for the obtained intrinsic mode functions resulting in discriminating features drawn from EEG channel pairs representing the two different lateral hemispheres. Numerical results suggest that the PLV induced from BEMD can effectively detect phase synchrony between electrodes and is a promising feature for BCI implementation.