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EEG-based brain functional connectivity representation using amplitude locking value for fatigue-driving recognition.

Ronglin Zheng1, Zhongmin Wang1,2, Yan He1,2

  • 1School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121 China.

Cognitive Neurodynamics
|April 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel functional synchronization analysis using electroencephalographic signals (EEG) to detect brain fatigue. The method accurately classifies fatigue states by analyzing brain network topology, offering a more comprehensive approach than traditional methods.

Keywords:
Amplitude locking valueElectroencephalographyFatigue drivingFunctional brain networks

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Network Science

Background:

  • Brain functional networks derived from electroencephalographic signals (EEG) exhibit changing topology with increasing brain fatigue.
  • Traditional methods using single signal components (amplitude or phase) for network construction offer a limited view of brain region relationships.

Purpose of the Study:

  • To develop a more comprehensive method for analyzing brain functional networks and characterizing brain fatigue.
  • To improve the classification accuracy of awake versus fatigue states using EEG signal analysis.

Main Methods:

  • Utilized empirical modal decomposition (EMD) to extract intrinsic mode components (IMFs) from EEG signals.
  • Applied Hilbert transform to obtain instantaneous amplitude and calculated Amplitude Locking Value (ALV) to measure synchronization between brain regions.
  • Constructed brain functional networks using ALV and analyzed topological properties, focusing on the Alpha band.

Main Results:

  • Achieved a classification accuracy of 82.84% for distinguishing between awake and fatigue states using discriminative connection features in the Alpha band.
  • Observed significantly increased inter-regional brain connectivity in the fatigue state compared to the awake state.
  • Demonstrated more orderly and efficient information interaction between brain regions in the awake state.

Conclusions:

  • The proposed functional synchronization analysis method provides a more robust characterization of brain fatigue compared to traditional approaches.
  • ALV-based functional brain networks effectively capture changes in brain state and offer potential for fatigue monitoring.
  • The findings highlight the importance of analyzing multi-component signal dynamics for understanding brain function and fatigue.