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An improved CapsNet based on data augmentation for driver vigilance estimation with forehead single-channel EEG.

Huizhou Yang1, Jingwen Huang1, Yifei Yu2

  • 1College of Information Science and Technology, Nanjing Forestry University, Nanjing, 210037 China.

Cognitive Neurodynamics
|December 16, 2024
PubMed
Summary

This study introduces a new algorithm for estimating driver vigilance using single-channel electroencephalography (EEG) signals. The method enhances accuracy and computing speed, making lightweight EEG vigilance estimation devices practical.

Keywords:
CGANCapsule networkCross-subjectSelf-attentionSingle-channel EEG signalsVigilance estimation

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Traffic Safety

Background:

  • Estimating driver vigilance is crucial for reducing traffic accidents.
  • Current electroencephalography (EEG)-based methods often require multi-channel signals, limiting practical application due to cost and complexity.

Purpose of the Study:

  • To develop a cross-subject vigilance estimation algorithm using single-channel EEG signals.
  • To improve the utility and accuracy of EEG-based vigilance monitoring for practical applications.

Main Methods:

  • A capsule network (CapsNet) algorithm was developed for single-channel EEG vigilance estimation.
  • Novel input feature map construction and a self-attention mechanism were integrated.
  • Conditional generative adversarial networks (cGANs) were used to augment single-channel data.

Main Results:

  • The proposed algorithm demonstrated improved computing speed.
  • Root-mean-square error (RMSE) was reduced by 3%, and Pearson Correlation Coefficient (PCC) improved by 12% compared to mainstream methods.
  • Feasibility of using single-channel forehead EEG for cross-subject vigilance estimation was confirmed.

Conclusions:

  • The developed CapsNet-based algorithm offers a feasible and efficient approach for cross-subject vigilance estimation using single-channel EEG.
  • This research paves the way for lightweight and practical EEG vigilance monitoring devices.