Electroencephalography (EEG) based drowsiness detection for drivers: A review

Vehicle accidents are rapidly increasing in many countries. Among many other factors, drowsiness is playing a major role in these accidents and systems which can monitor it are currently being developed. Among them, Electroencephalography (EEG) proved to be very reliable. Indeed, many EEG based drow...

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Main Authors: Shameen, Z., Yusoff, M.Z., Saad, M.N.M., Malik, A.S., Muzammel, M.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.21821 /
Published: Asian Research Publishing Network 2018
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042665528&partnerID=40&md5=fc717c4813813fa519ee3e6b598bfa1c
http://eprints.utp.edu.my/21821/
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spelling utp-eprints.218212018-11-16T08:31:07Z Electroencephalography (EEG) based drowsiness detection for drivers: A review Shameen, Z. Yusoff, M.Z. Saad, M.N.M. Malik, A.S. Muzammel, M. Vehicle accidents are rapidly increasing in many countries. Among many other factors, drowsiness is playing a major role in these accidents and systems which can monitor it are currently being developed. Among them, Electroencephalography (EEG) proved to be very reliable. Indeed, many EEG based drowsiness detection techniques are proposed for drivers. Most of these drowsiness detection techniques are normally subdivided into feature extraction and classification methods. Features obtained from FFT are effective and give higher accuracy; but are limited by the non stationary behavior of EEG signals. This paper reviews some of the most recent work of the EEG based drowsiness detection techniques. It shows a major gap found in all these studies, which is the fact that the channel selection method is not clearly specified. Therefore, research can be undertaken to properly choose suitable channel(s) to realize accurate detection of drowsiness. This survey also highlights the fact that, there is no publicly available data and comparison between techniques is not yet possible, because each technique is tested on its own dataset. © 2006-2018 Asian Research Publishing Network (ARPN). Asian Research Publishing Network 2018 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042665528&partnerID=40&md5=fc717c4813813fa519ee3e6b598bfa1c Shameen, Z. and Yusoff, M.Z. and Saad, M.N.M. and Malik, A.S. and Muzammel, M. (2018) Electroencephalography (EEG) based drowsiness detection for drivers: A review. ARPN Journal of Engineering and Applied Sciences, 13 (4). pp. 1458-1464. http://eprints.utp.edu.my/21821/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Vehicle accidents are rapidly increasing in many countries. Among many other factors, drowsiness is playing a major role in these accidents and systems which can monitor it are currently being developed. Among them, Electroencephalography (EEG) proved to be very reliable. Indeed, many EEG based drowsiness detection techniques are proposed for drivers. Most of these drowsiness detection techniques are normally subdivided into feature extraction and classification methods. Features obtained from FFT are effective and give higher accuracy; but are limited by the non stationary behavior of EEG signals. This paper reviews some of the most recent work of the EEG based drowsiness detection techniques. It shows a major gap found in all these studies, which is the fact that the channel selection method is not clearly specified. Therefore, research can be undertaken to properly choose suitable channel(s) to realize accurate detection of drowsiness. This survey also highlights the fact that, there is no publicly available data and comparison between techniques is not yet possible, because each technique is tested on its own dataset. © 2006-2018 Asian Research Publishing Network (ARPN).
format Article
author Shameen, Z.
Yusoff, M.Z.
Saad, M.N.M.
Malik, A.S.
Muzammel, M.
spellingShingle Shameen, Z.
Yusoff, M.Z.
Saad, M.N.M.
Malik, A.S.
Muzammel, M.
Electroencephalography (EEG) based drowsiness detection for drivers: A review
author_sort Shameen, Z.
title Electroencephalography (EEG) based drowsiness detection for drivers: A review
title_short Electroencephalography (EEG) based drowsiness detection for drivers: A review
title_full Electroencephalography (EEG) based drowsiness detection for drivers: A review
title_fullStr Electroencephalography (EEG) based drowsiness detection for drivers: A review
title_full_unstemmed Electroencephalography (EEG) based drowsiness detection for drivers: A review
title_sort electroencephalography (eeg) based drowsiness detection for drivers: a review
publisher Asian Research Publishing Network
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042665528&partnerID=40&md5=fc717c4813813fa519ee3e6b598bfa1c
http://eprints.utp.edu.my/21821/
_version_ 1741196526286274560
score 11.62408

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