An improved method to calculate phase locking value based on Hilbert–Huang transform and its application

Abstract

Phase synchronization analysis has been demonstrated to be a useful method to infer brain function and neural activity based on electroencephalography (EEG) signals. The phase locking value (PLV) is one of the most important tools for phase synchronization analysis. Although the traditional method (TM) to calculate PLV, which is based on the Hilbert transform, has been applied extensively, some of methodological problems of TM have not been solved. To address these problems, this paper proposes an improved method (IM) to calculate the PLV based on the Hilbert–Huang transform. For the IM, the Hilbert–Huang transform, instead of the Hilbert transform, is used to process non-stationary EEG signals and the empirical mode decomposition, not band-pass filter, is used to get target frequency band. The performance of the IM is evaluated by comparing normal and hypoxia EEG signals. The PLVs are used as features for a least squares support vector machine to recognize normal and hypoxia EEG. Experimental results show that the PLVs calculated by the IM can distinguish the EEG signals better than those calculated by TM.