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Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review

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dc.contributor.author Muhammad Shafiq Ibrahim
dc.contributor.author Seri Rahayu Kamat
dc.contributor.author Syamimi Shamsuddin
dc.contributor.author Mohd Hafzi Md Isa
dc.contributor.author Momoyo Ito
dc.date.accessioned 2025-05-28T07:34:48Z
dc.date.available 2025-05-28T07:34:48Z
dc.date.issued 2022-03
dc.description.abstract An efficient system that is capable to detect driver fatigue is urgently needed to help avoid road crashes. Recently, there has been an increase of interest in the application of electroencephalogram (EEG) to detect driver fatigue. Feature extraction and signal classification are the most critical steps in the EEG signal analysis. A reliable method for feature extraction is important to obtain robust signal classification. Meanwhile, a robust algorithm for signal classification will accurately classify the feature to a particular class. This paper concisely reviews the pros and cons of the existing techniques for feature extraction and signal classification and its fatigue detection accuracy performance. The integration of combined entropy (feature extraction) with support vector machine (SVM) and random forest (classifier) gives the best fatigue detection accuracy of 98.7% and 97.5% respectively. The outcomes from this study will guide future researchers in choosing a suitable technique for feature extraction and signal classification for EEG data processing and shed light on directions for future research and development of driver fatigue countermeasures.
dc.identifier.uri https://ijneam.unimap.edu.my/
dc.identifier.uri https://hdl.handle.net/20.500.14170/13895
dc.language.iso en
dc.publisher Universiti Malaysia Perlis (UniMAP)
dc.relation.ispartof International Journal of Nanoelectronics and Materials (IJNeaM)
dc.relation.issn 1985-5761
dc.subject Driver fatigue
dc.subject Electroencephalogram (EEG)
dc.subject Feature extraction
dc.subject Signal classification
dc.title Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review
dc.type Resource Types::text::journal::journal article
dspace.entity.type Publication
oaire.citation.endPage 380
oaire.citation.issue Special Issue
oaire.citation.startPage 365
oaire.citation.volume 15
oairecerif.author.affiliation Universiti Teknikal Malaysia Melaka
oairecerif.author.affiliation Universiti Teknikal Malaysia Melaka
oairecerif.author.affiliation Universiti Teknikal Malaysia Melaka
oairecerif.author.affiliation Malaysian Institute of Road Safety Research (MIROS)
oairecerif.author.affiliation Tokushima University
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