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Related Concept Videos

Narcolepsy01:07

Narcolepsy

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Narcolepsy is a chronic sleep disorder characterized by pervasive, uncontrolled sleepiness and other sleep disturbances. One of its hallmark symptoms is an abrupt transition to REM sleep upon falling asleep, which causes symptoms typically associated with this phase to occur unexpectedly during wakefulness. These include the following symptoms, which typically last from a minute or two to half an hour.
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Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review.

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  • 1School of Psychology and Neuroscience, University of Glasgow, Glasgow G12 8QB, UK.

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Summary

This review explores electroencephalogram (EEG)-based algorithms for detecting driver drowsiness, a critical safety issue for both traditional and self-driving cars. It proposes a framework for superior drowsiness detection systems.

Keywords:
EEGbrain stimulationclosed-loop algorithmsdrivers’ drowsiness detectionmachine learning

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Area of Science:

  • Neuroscience
  • Automotive Engineering
  • Human-Computer Interaction

Background:

  • Driver drowsiness poses significant safety risks in traditional driving.
  • Drowsiness is also a key challenge for the adoption of self-driving cars, manifesting as carsickness.
  • Early detection of drowsiness is crucial for driver safety and vehicle system acceptance.

Purpose of the Study:

  • To review and organize electroencephalogram (EEG)-based algorithms for driver drowsiness detection (DDD).
  • To identify key questions and develop a superior EEG-based DDD system.
  • To establish a taxonomy for EEG-based DDD approaches.

Main Methods:

  • Systematic review of EEG-based driver drowsiness detection algorithms.
  • Organization of algorithms into a tree structure taxonomy: 'detection only (open-loop)' and 'management (closed-loop)'.
  • Addressing seven key questions to guide the development of improved DDD systems.

Main Results:

  • A comprehensive review and classification of existing EEG-based DDD algorithms.
  • Identification of pathways to enhance DDD systems for early detection, reliability, and practical utility.
  • A foundational framework for developing advanced EEG-based drowsiness detection.

Conclusions:

  • EEG-based DDD is essential for enhancing driving safety in both conventional and autonomous vehicles.
  • A structured approach to reviewing DDD algorithms facilitates the development of more effective systems.
  • The underlying brain network for drowsiness is consistent, regardless of the cause (e.g., fatigue vs. carsickness).