Driving Fatigue Effects on Cross-Frequency Phase Synchrony Embedding in Multilayer Brain Network

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 electroencephalogram (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 subnetworks 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 fatigue-related studies for a better understanding about the underlying mechanism and ultimately a traffic accident reduction.

Automated summary

This study examined the neural mechanism of driver fatigue by investigating the cross-frequency coupling of slow and fast oscillations in a multilayer brain network. It was found that the coupling in the fatigue state was enhanced, particularly in beta-gamma coupling and the frontal, frontal pole, and parietal regions. Significant differences were also observed in the topology of the multilayer brain network between vigilant and fatigue states, including increased global and local efficiencies in the fatigue state. A graph neural network (GNN) was developed to detect fatigue with high accuracy (96.23%) by imitating the features of the within-frequency subnetworks diffused through cross-frequency coupling. This research provides insights into neural coordination in driver fatigue and can contribute to reducing traffic accidents.