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Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals.

Yingmei Qin1, Ziyu Hu1, Yi Chen1

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

Entropy (Basel, Switzerland)
|August 26, 2022
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Summary

Driving fatigue impairs brain networks, increasing local information processing while decreasing global efficiency. This study reveals how fatigue affects brain connectivity, potentially explaining neural mechanisms behind traffic accidents.

Keywords:
EEGcausal flowcurrent source densitydirected networkfatigue drivinginformation integration

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Area of Science:

  • Neuroscience
  • Traffic Safety
  • Cognitive Psychology

Background:

  • Driving fatigue is a significant cause of traffic accidents.
  • Monotonous driving reduces driver attention and vigilance.
  • Understanding the neural basis of driving fatigue is crucial for accident prevention.

Purpose of the Study:

  • To investigate the impact of driving fatigue on the brain's information processing capabilities.
  • To analyze changes in directed brain networks using electroencephalogram (EEG) source signals.
  • To identify neural mechanisms underlying driving fatigue.

Main Methods:

  • Utilized electroencephalogram (EEG) signals to derive source analysis data (Current Source Density).
  • Constructed a directed brain network for fatigue driving using a directed transfer function.
  • Performed causal flow analysis to identify differences between awake and fatigued states.

Main Results:

  • Increased average clustering coefficient and path length with prolonged driving, indicating enhanced local integration.
  • Decreased global efficiency in most brain rhythms, suggesting weakened global information processing.
  • Significant differences in causal flow, particularly in anterior and posterior regions under the theta rhythm, with impaired information transfer from posterior to anterior regions.

Conclusions:

  • Driving fatigue enhances local brain information integration while diminishing global integration.
  • Altered brain network dynamics, especially in specific regions and rhythms, are associated with driving fatigue.
  • Findings provide insights into the neural mechanisms of driving fatigue, potentially informing safety interventions.