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Driver drowsiness detection methods using EEG signals: a systematic review.

Raed Mohammed Hussein1, Firas Sabar Miften2, Loay E George3

  • 1Iraqi Commission for Computers and Informatics, Informatics Institute of Postgraduate Studies, Baghdad, Iraq.

Computer Methods in Biomechanics and Biomedical Engineering
|August 19, 2022
PubMed
Summary

Detecting driver drowsiness using electroencephalography (EEG) signals is crucial for road safety. This review of 62 studies highlights trends in EEG data analysis and classification methods for driver fatigue detection.

Keywords:
Drowsy driving detectionelectrical activityelectroencephalographymachine learningmedical signal processingpower spectral density

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Area of Science:

  • Neuroscience and Cognitive Science
  • Transportation Safety
  • Machine Learning Applications

Background:

  • Electroencephalography (EEG) signal interpretation requires specialized expertise and advanced signal processing techniques.
  • Driver drowsiness poses a significant risk, necessitating effective detection methods.
  • Existing research on EEG for driver drowsiness detection has rapidly evolved between 2018 and 2022.

Approach:

  • A systematic literature review of 62 papers published between January 2018 and 2022 was conducted.
  • Data extraction focused on experimental setup, EEG channels, signal processing, classification methods, and outcomes.
  • Major scientific databases were searched to identify relevant studies in journals, conferences, and preprints.

Key Points:

  • Variability in the amount of EEG data utilized across studies was observed.
  • Over 50% of reviewed studies employed simulated driving experiments.
  • Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) were prominent classification methods, used in 21% and 19% of studies, respectively.

Conclusions:

  • Driver drowsiness and fatigue demonstrably impair driving performance, increasing accident risk.
  • The review identifies key trends and methodologies in EEG-based driver drowsiness detection.
  • Findings provide a foundation for future research and recommendations in this critical safety domain.