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Brain Complex Network Characteristic Analysis of Fatigue during Simulated Driving Based on Electroencephalogram

Chunxiao Han1, Xiaozhou Sun1, Yaru Yang1

  • 1Tianjin Key Laboratory of Information Sensing & Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222, China.

Entropy (Basel, Switzerland)
|December 3, 2020
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Summary

Driving fatigue significantly alters brain network dynamics, particularly in the delta rhythm. This research may help identify objective measures for detecting driver fatigue and preventing accidents.

Keywords:
EEGclustering coefficientcomplex networkdegree centralitydriving fatiguefunctional connectivityshortest path length

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Area of Science:

  • Neuroscience
  • Traffic Safety
  • Complex Systems Analysis

Background:

  • Driver fatigue is a major cause of traffic accidents.
  • Prolonged driving can lead to fatigue, impacting the central nervous system.

Purpose of the Study:

  • To investigate the effects of driving fatigue on brain dynamics using electroencephalogram (EEG) signals.
  • To apply complex network theory to analyze changes in brain activity during simulated driving.

Main Methods:

  • A fatigue driving simulation experiment was conducted.
  • Electroencephalogram (EEG) signals were collected during the simulation.
  • Complex network theory was used to analyze EEG data, focusing on different rhythms and brain regions.

Main Results:

  • As driver fatigue increased, functional connectivity and clustering coefficients rose, while the average shortest path length decreased in the delta rhythm.
  • Significant increases in degree centrality were observed in specific right-brain channels for the delta rhythm.

Conclusions:

  • Driving fatigue causes significant changes in brain complex network characteristics, particularly for the delta rhythm and certain brain regions.
  • These findings could form the basis for objective indicators to assess driver fatigue and enhance road safety.