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Partial directed coherence based graph convolutional neural networks for driving fatigue detection.

Weiwei Zhang1, Fei Wang1, Shichao Wu1

  • 1Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China.

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Summary
This summary is machine-generated.

This study introduces a novel Partial Directed Coherence Graph Convolutional Neural Network (PDC-GCNN) for detecting driver fatigue using electroencephalogram (EEG) signals. The method analyzes brain functional connections, improving accuracy in fatigue detection systems.

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Transportation Safety

Background:

  • Driver mental state evaluation relies on electroencephalogram (EEG) signals.
  • Traditional methods often overlook brain functional connections, and existing network methods require manual feature extraction.
  • There is a need for automated and comprehensive analysis of EEG for driving fatigue detection.

Purpose of the Study:

  • To propose a novel method for driving fatigue detection that integrates single-electrode analysis with brain network topological features.
  • To improve the accuracy and efficiency of detecting driver fatigue by analyzing EEG signals.

Main Methods:

  • Development of a Partial Directed Coherence Graph Convolutional Neural Network (PDC-GCNN).
  • Utilizing Partial Directed Coherence (PDC) to construct an adjacency matrix representing EEG channel relationships.
  • Combining single-electrode features (differential entropy and power spectral density) with graph convolutional networks (GCNN) for automated topological feature extraction.

Main Results:

  • The PDC-GCNN achieved an average recognition accuracy of 84.32% (DE) and 83.84% (PSD) in ten-fold cross-validation.
  • Subject-specific experiments showed high accuracy rates of 96.01% (DE) and 95.24% (PSD).
  • The method effectively analyzes both single-electrode characteristics and brain network connectivity.

Conclusions:

  • The proposed PDC-GCNN method offers a robust and automated approach to driving fatigue detection.
  • This research has significant practical application value for integration into vehicle driving fatigue detection systems.
  • The findings highlight the importance of considering brain functional connectivity in EEG-based mental state analysis.