Robust analysis and spectral-based deep learning to detect driving fatigue from EEG signals

Antonio Quintero-Rincón, Lotfi Chaari, Hadj Batatia

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Driver fatigue is a major cause of traffic accidents. Electroencephalogram (EEG) is considered one of the most reliable predictors of fatigue. This paper proposes a novel, simple and fast method for driver fatigue detection that can be implemented in real-time by using a single-channel on the scalp. The study has two objectives. The first consists of determining the single most relevant EEG channel to monitor fatigue. This is done using maximum covariance analysis. The second objective consists in developing a deep learning method to detect fatigue from this single channel. For this purpose, spectral features of the signal are first extracted. The sequence of features is used to train a Long Short Term Memory (LSTM), deep learning model, to detect fatigue states. Experiments with 12 EEG signals were conducted to discriminate the fatigue stage from the alert stage. Results showed that TP7 was the most significant channel, which is located in the left tempo-parietal region. A zone associated with spatial awareness, visual-spatial navigation, and the cautiousness faculty. In addition, despite the small dataset, the proposed method predicts fatigue with 75% accuracy and a 1.4-second delay. These promising results provide new insights into relevant data for monitoring driver fatigue.
Original languageEnglish
Title of host publication2022 International Conference on Technology Innovations for Healthcare (ICTIH)
ISBN (Electronic)9798350334241
Publication statusPublished - 4 May 2023


  • biLSTM
  • EEG
  • Fatigue
  • Robust analysis

ASJC Scopus subject areas

  • Information Systems and Management
  • Health(social science)
  • Artificial Intelligence
  • Health Informatics
  • Biomedical Engineering
  • Computer Science Applications


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