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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Graph analysis of functional brain network topology using minimum spanning tree in driver drowsiness.

Jichi Chen1, Hong Wang1, Chengcheng Hua1

  • 1Department of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110819 Liaoning China.

Cognitive Neurodynamics
|November 29, 2018
PubMed
Summary

Driver drowsiness alters functional brain network topology. Alertness shows a line-like network, while drowsiness exhibits a star-like configuration, particularly in lower frequency bands, aiding in accident prevention.

Keywords:
Driver drowsinessElectroencephalography (EEG)Functional connectivityGraph theoryMinimum spanning tree

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Area of Science:

  • Neuroscience
  • Transportation Safety
  • Signal Processing

Background:

  • Driver drowsiness is a significant cause of traffic accidents globally.
  • Understanding the brain network topology changes associated with drowsiness is crucial for developing effective detection methods.

Purpose of the Study:

  • To introduce a novel approach for detecting driver drowsiness using electroencephalogram (EEG) signals.
  • To investigate how brain network topology is modulated by drowsiness during a simulated driving task.

Main Methods:

  • EEG signals were recorded from participants in simulated driving tasks (alert vs. drowsy states).
  • Wavelet packet transform decomposed EEG signals into frequency bands; functional connectivity was assessed using the phase lag index (PLI).
  • Minimum spanning trees were constructed from PLI-weighted networks, and graph-derived metrics were statistically analyzed.

Main Results:

  • Significant differences in graph metrics were observed in delta and theta frequency bands between alert and drowsy states.
  • Network integration and communication increased from alertness to drowsiness.
  • Alert states showed a more line-like network topology, while drowsy states exhibited a more star-like topology.

Conclusions:

  • Graph metrics derived from EEG functional connectivity can effectively differentiate between alert and drowsy states.
  • The findings provide insights into the neural mechanisms of driver drowsiness.
  • This approach may enhance driver drowsiness detection systems to reduce traffic accidents.