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

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 s...

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Main Authors: Kamat, Seri Rahayu, Ibrahim, Muhammad Shafiq, Shamsuddin, Syamimi, Md Isa, Mohd Hafzi, Ito, Momoyo
Format: Article
Language:English
Published: Universiti Malaysia Perlis 2022
Online Access:http://eprints.utem.edu.my/id/eprint/26571/2/0.%20ELECTROENCEPHALOGRAM%20%28EEG%29-BASED%20SYSTEMS%20TO%20MONITOR%20DRIVER%20FATIGUE.PDF
http://eprints.utem.edu.my/id/eprint/26571/
http://dspace.unimap.edu.my/bitstream/handle/123456789/76070/Vol_15_SI_March_2022_365-380.pdf?sequence=1&isAllowed=y
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spelling my.utem.eprints.265712023-03-07T08:01:37Z http://eprints.utem.edu.my/id/eprint/26571/ Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review Kamat, Seri Rahayu Ibrahim, Muhammad Shafiq Shamsuddin, Syamimi Md Isa, Mohd Hafzi Ito, Momoyo 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. Universiti Malaysia Perlis 2022-03 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26571/2/0.%20ELECTROENCEPHALOGRAM%20%28EEG%29-BASED%20SYSTEMS%20TO%20MONITOR%20DRIVER%20FATIGUE.PDF Kamat, Seri Rahayu and Ibrahim, Muhammad Shafiq and Shamsuddin, Syamimi and Md Isa, Mohd Hafzi and Ito, Momoyo (2022) Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review. International Journal of Nanoelectronics and Materials, 15 (SI). 365 - 380. ISSN 1985-5761 http://dspace.unimap.edu.my/bitstream/handle/123456789/76070/Vol_15_SI_March_2022_365-380.pdf?sequence=1&isAllowed=y
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description 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.
format Article
author Kamat, Seri Rahayu
Ibrahim, Muhammad Shafiq
Shamsuddin, Syamimi
Md Isa, Mohd Hafzi
Ito, Momoyo
spellingShingle Kamat, Seri Rahayu
Ibrahim, Muhammad Shafiq
Shamsuddin, Syamimi
Md Isa, Mohd Hafzi
Ito, Momoyo
Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review
author_facet Kamat, Seri Rahayu
Ibrahim, Muhammad Shafiq
Shamsuddin, Syamimi
Md Isa, Mohd Hafzi
Ito, Momoyo
author_sort Kamat, Seri Rahayu
title Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review
title_short Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review
title_full Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review
title_fullStr Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review
title_full_unstemmed Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review
title_sort electroencephalogram (eeg)-based systems to monitor driver fatigue: a review
publisher Universiti Malaysia Perlis
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/26571/2/0.%20ELECTROENCEPHALOGRAM%20%28EEG%29-BASED%20SYSTEMS%20TO%20MONITOR%20DRIVER%20FATIGUE.PDF
http://eprints.utem.edu.my/id/eprint/26571/
http://dspace.unimap.edu.my/bitstream/handle/123456789/76070/Vol_15_SI_March_2022_365-380.pdf?sequence=1&isAllowed=y
_version_ 1759693077538144256
score 13.211869

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