A review of EEG-based driver fatigue detection using deep learning: breakthroughs and progress in 2024

Rodney Petrus Balandong and Ammielle Akim Kerudin (2024) A review of EEG-based driver fatigue detection using deep learning: breakthroughs and progress in 2024.

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Abstract

Driver fatigue significantly contributes to road accidents worldwide, necessitating effective detection systems. Electroencephalography (EEG) is the gold standard for monitoring driver fatigue due to its direct assessment of brain activity. This paper reviews recent advancements in EEG-based driver fatigue detection systems, focusing on innovations achieved in 2024. The literature highlights the development of deep learning architectures that aim to enhance feature extraction and classification accuracy. These advancements demonstrate significant improvements in detection accuracy, system efficiency, and real-world applicability, contributing to safer driving environments by enabling timely intervention strategies.

Item Type:	Proceedings
Keyword:	Driving fatigue, Deep learning, Electroencephalography
Subjects:	Q Science > Q Science (General) > Q1-390 Science (General) > Q1-295 General R Medicine > RC Internal medicine > RC31-1245 Internal medicine > RC321-571 Neurosciences. Biological psychiatry. Neuropsychiatry > RC346-429 Neurology. Diseases of the nervous system Including speech disorders
Department:	FACULTY > Faculty of Science and Natural Resources
Depositing User:	SITI AZIZAH BINTI IDRIS -
Date Deposited:	19 Mar 2025 14:11
Last Modified:	19 Mar 2025 14:11
URI:	https://eprints.ums.edu.my/id/eprint/43257

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