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EEG classification of driver mental states by deep learning.

Hong Zeng1, Chen Yang1, Guojun Dai1

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

Cognitive Neurodynamics
|November 29, 2018
PubMed
Summary

New deep learning models, EEG-Conv and EEG-Conv-R, accurately predict driver mental states from electroencephalography (EEG) signals, outperforming traditional methods for safer roads.

Keywords:
Driver fatigueEEG-ConvEEG-Conv-RElectroencephalography (EEG)Residual learning

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Transportation Safety

Background:

  • Driver fatigue is a primary cause of traffic accidents, posing significant societal and familial risks.
  • Accurate prediction of driver mental states is crucial for enhancing road safety.

Purpose of the Study:

  • To develop and evaluate novel deep learning models for predicting driver mental states using electroencephalography (EEG) signals.
  • To compare the performance of these new models against traditional machine learning classifiers.

Main Methods:

  • Development of two deep convolutional neural network models: EEG-Conv and EEG-Conv-R, incorporating deep residual learning.
  • Testing the models on both intra-subject and inter-subject datasets.
  • Comparison with Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) classifiers.

Main Results:

  • Both EEG-Conv and EEG-Conv-R demonstrated superior classification performance for mental state prediction compared to LSTM and SVM.
  • EEG-Conv-R showed particular suitability for inter-subject mental state prediction.
  • EEG-Conv-R exhibited faster convergence rates than EEG-Conv.

Conclusions:

  • The proposed EEG-Conv and EEG-Conv-R classifiers offer enhanced predictive power for driver mental states.
  • These models show significant promise for practical applications in brain-computer interaction systems for driver monitoring.