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

Rongrong Fu; Mengmeng Han; Bao Yu; Peiming Shi; Jiangtao Wen

doi:10.7307/ptt.v32i4.3303

Rongrong Fu Key Lab of Hebei Province, Yanshan University, Qinhuangdao, China
Mengmeng Han Key Lab of Hebei Province, Yanshan University, Qinhuangdao, China
Bao Yu Key Lab of Hebei Province, Yanshan University, Qinhuangdao, China
Peiming Shi Key Lab of Hebei Province, Yanshan University, Qinhuangdao, China
Jiangtao Wen Key Lab of Hebei Province, Yanshan University, Qinhuangdao, China

DOI: https://doi.org/10.7307/ptt.v32i4.3303

Keywords: electroencephalogram (EEG), weighted brain networks, driver fatigue

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.

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