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An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction.

Hong Zeng1,2,3, Xiufeng Li1, Gianluca Borghini3

  • 1School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary

Detecting driver fatigue using electroencephalogram (EEG) signals is challenging due to individual differences. A new Generative-DANN model improves cross-subject fatigue detection accuracy to 91.63%.

Keywords:
Domain-Adversarial Neural Network (DANN)Generative Adversarial Networks (GAN)cross-subject predictionelectroencephalogram (EEG)

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Traffic Safety

Background:

  • Fatigued driving is a major cause of traffic accidents.
  • Electroencephalogram (EEG)-based mental state analysis offers an objective method for fatigue detection.
  • Cross-subject EEG analysis presents a significant challenge due to inter-subject variability.

Purpose of the Study:

  • To develop an advanced model for accurate cross-subject fatigue detection using EEG signals.
  • To address the limitations of traditional models in handling inter-subject EEG data distribution differences.
  • To explore the application of Domain-Adversarial Neural Network (DANN) in EEG-based fatigue detection.

Main Methods:

  • Introduction of Generative-DANN (GDANN), a novel model combining Generative Adversarial Networks (GAN) with DANN.
  • Application of GDANN to address the distribution discrepancies in cross-subject EEG data.
  • Comparative analysis of GDANN against traditional classification models for fatigue detection.

Main Results:

  • GDANN achieved a high average accuracy of 91.63% in cross-subject fatigue detection.
  • The proposed model demonstrated superior performance compared to traditional classification methods.
  • The integration of GAN enhanced the model's ability to handle diverse EEG data distributions.

Conclusions:

  • GDANN offers a promising solution for robust and accurate cross-subject EEG-based fatigue detection.
  • The model shows significant potential for practical applications in brain-computer interaction (BCI).
  • This research advances the field of fatigue monitoring systems for improved road safety.