Electroencephalogram-based approaches for driver drowsiness detection and management: a review

Li, G. and Chung, W.-Y. (2022) Electroencephalogram-based approaches for driver drowsiness detection and management: a review. Sensors, 22(3), 1100. (doi: 10.3390/s22031100) (PMID:35161844) (PMCID:PMC8840041)

[img] Text
264493.pdf - Published Version
Available under License Creative Commons Attribution.



Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early-stage symptoms of self-driving carsickness). In view of the importance of detecting drivers’ drowsiness, this paper reviews the algorithms of electroencephalogram (EEG)-based drivers’ drowsiness detection (DDD). To facilitate the review, the EEG-based DDD approaches are organized into a tree structure taxonomy, having two main categories, namely “detection only (open-loop)” and “management (closed-loop)”, both aimed at designing better DDD systems that ensure early detection, reliability and practical utility. To achieve this goal, we addressed seven questions, the answers of which helped in developing an EEG-based DDD system that is superior to the existing ones. A basic assumption in this review article is that although driver drowsiness and carsickness-induced drowsiness are caused by different factors, the brain network that regulates drowsiness is the same.

Item Type:Articles
Additional Information:This research was funded by the framework of international cooperation program man‐ aged by the National Research Foundation of Korea, grant number NRF‐ 2019K1A3A1A0505088484 and the National Natural Science Foundation of China, grant number 61901264.
Keywords:Drivers’ drowsiness detection, EEG, machine learning, brain stimulation, closed-loop algorithms.
Glasgow Author(s) Enlighten ID:Li, Dr Gang
Authors: Li, G., and Chung, W.-Y.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:Sensors
ISSN (Online):1424-8220
Published Online:31 January 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Sensors 22(3): 1100
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record