Driving Fatigue Effects on Cross-Frequency Phase Synchrony Embedding in Multilayer Brain Network
Shun Liu, Chi Man Wong, Xucheng Liu, Hongtao Wang, Anastasios Bezerianos, Yu Sun, Tzyy‐Ping Jung, Feng Wan
Abstract
Driver fatigue has been intensively investigated for recent decades, nevertheless the underlying neural mechanism remains unclear. This study explored the cross-frequency coupling (CFC) between slow and fast oscillations in a multilayer brain network description of the functional brain network. Specifically, we compared the topological characteristics of the CFC embedded multilayer brain networks in the vigilant and fatigue states. From the 24-channel EEG recorded on 20 subjects, we found that the CFC of the fatigue state was elevated, especially in the beta-gamma coupling and in the frontal pole, frontal, and parietal regions. Results also revealed profound differences in the topology of the multilayer brain network between the vigilant and fatigue states, particularly the significant increases in the global and local efficiencies of the multilayer network in the fatigue state that were closely related to the behavioral performance, i.e., the reaction time. What is more, a graph neural network (GNN) was developed for imitating the features of the within-frequency sub-networks diffused through the CFC to detect fatigue with a satisfactory classification accuracy (96.23%). The proposed approach could enhance our understanding about neural coordination across frequencies in driver fatigue and would facilitate the fatigue-related studies for a better understanding about the underlying mechanism and ultimately a traffic accident reduction.