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Investigating Driver Fatigue versus Alertness Using the Granger Causality Network.

Wanzeng Kong1,2, Weicheng Lin3, Fabio Babiloni4

  • 1College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China. kongwanzeng@hdu.edu.cn.

Sensors (Basel, Switzerland)
|August 8, 2015
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Summary

Detecting driver fatigue is crucial for safety. This study found changes in brain network activity, specifically in causal flow and information integration, indicating drowsiness.

Keywords:
brain effective networkdriving fatigueeegfrequency domaingranger causality

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Area of Science:

  • Neuroscience
  • Transportation Safety
  • Cognitive Science

Background:

  • Driving fatigue is a major safety concern.
  • Objective detection of driver fatigue is needed.
  • Brain network analysis offers potential for fatigue detection.

Purpose of the Study:

  • To identify neurometric indicators of driver fatigue.
  • To analyze changes in brain networks related to alertness and drowsiness.
  • To investigate the relationship between brain effective networks and fatigue levels.

Main Methods:

  • Electroencephalogram (EEG) signals recorded from 12 subjects during simulated driving.
  • Analysis of brain effective networks using Granger-Causality (GC).
  • Comparison of network topology and information integration between alert and drowsy states.

Main Results:

  • Significant differences in Granger causality strength and brain network properties (causal flow, global efficiency, path length) between alert and drowsy states.
  • Changes in brain network topology and information integration capacity were observed.
  • Frontal lobe activity in the alpha frequency band showed significant changes related to fatigue.

Conclusions:

  • Brain effective network analysis can detect driver fatigue.
  • Changes in network properties provide a neurometric indicator for fatigue levels.
  • Findings support the development of fatigue detection systems for enhanced road safety.