Identifying Intrinsic Phase Lag in EEG Signals from the Perspective of Wilcoxon Signed-Rank Test

Abstract

In brain functional network connectivity analysis, phase synchronization has been effective in detecting regions demonstrating similar dynamics over time. The previously proposed connectivity indices such as phase locking value (PLV), phase lag index (PLI) and weighted phase lag index (WPLI) are widely used. They are, however, influenced by volume conduction or noise. In addition, appropriate thresholds have to be chosen in order to employ them successfully, which leads to uncertainty. In this paper, a novel connectivity index named phase lag based on the Wilcoxon signed-rank test (PLWT) is proposed under the framework of Wilcoxon signed-rank test, which avoids using thresholds to identify effective connections. We analyzed and compared PLWT with previous indices by simulating volume conduction and testing the scale-free character of brain networks constructed based on EEG signals. The experimental results demonstrated that PLWT can be utilized as a reliable and convincing measure to reveal true connections while effectively diminishing the influence of volume conduction.