Phase Fluctuation Analysis in Functional Brain Networks of Scaling EEG for Driver Fatigue Detection

Abstract

The characterization of complex patterns arising from electroencephalogram (EEG) is an important problem with significant applications in identifying different mental states. Based on the operational EEG of drivers, a method is proposed to characterize and distinguish different EEG patterns. The EEG measurements from seven professional taxi drivers were collected under different states. The phase characterization method was used to calculate the instantaneous phase from the EEG measurements. Then, the optimization of drivers’ EEG was realized through performing common spatial pattern analysis. The structures and scaling components of the brain networks from optimized EEG measurements are sensitive to the EEG patterns. The effectiveness of the method is demonstrated, and its applicability is articulated.

Optimal feature‐algorithm combination research for EEG fatigue driving detection based on functional brain network

“…They concluded that combining the MST approach with the SL approach improved the classification accuracy of all the classifiers used in the study, especially the KNN classifier [27]. Some researchers used the phase characterisation method to calculate the instantaneous phase of EEG signals, optimised the EEG signal data by spatial model analysis, and demonstrated the method's effectiveness [28]. Some researchers used Partial Directed Coherence (PDC) to calculate the correlation between EEG channels and used SVM as a classifier for the critical EEG electrodes and frequency bands for fatigue driving detection and obtained the conclusion that the low-frequency bands were better identified than the high-frequency bands.…”

“…Functional brain network [27][28][29][30][31][32] describes the statistically significant connection relationship between the nodes of the brain network, does not reflect the causal relationship between the nodes, and is an undirected network. It is based on the electromagnetic signal and kinetic signal of the brain network etc.…”