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

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


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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How to Cite
Fu R, Han M, Yu B, Shi P, Wen J. Phase Fluctuation Analysis in Functional Brain Networks of Scaling EEG for Driver Fatigue Detection. Promet - Traffic&Transportation. 2020;32(4):487-95. DOI: 10.7307/ptt.v32i4.3303