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

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Main Authors: Seri Rahayu, Kamat, Muhammad Shafiq, Ibrahim, Syamimi, Shamsuddin
Other Authors: seri@utem.edu.my
Format: Article
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2022
Subjects:
Driver fatigue
Electroencephalogram (EEG)
Feature extraction
Signal classification
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/76070
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spelling my.unimap-760702022-08-24T01:17:51Z Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review Seri Rahayu, Kamat Muhammad Shafiq, Ibrahim Seri Rahayu, Kamat Syamimi, Shamsuddin seri@utem.edu.my Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka (UTeM) Work-Related Road Safety Management Cluster, Malaysian Institute of Road Safety Research (MIROS) Information Science and Intelligent Systems, Tokushima University Driver fatigue Electroencephalogram (EEG) Feature extraction Signal classification Link to publisher's homepage at http://ijneam.unimap.edu.my 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. 2022-08-24T01:17:51Z 2022-08-24T01:17:51Z 2022-03 Article International Journal of Nanoelectronics and Materials, vol.15 (Special Issue), 2022, pages 365-380 2232-1535 (online) 1985-5761 (Printed) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/76070 http://ijneam.unimap.edu.my en Special Issue ISSTE 2022; Universiti Malaysia Perlis (UniMAP)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Driver fatigue
Electroencephalogram (EEG)
Feature extraction
Signal classification
spellingShingle Driver fatigue
Electroencephalogram (EEG)
Feature extraction
Signal classification
Seri Rahayu, Kamat
Muhammad Shafiq, Ibrahim
Seri Rahayu, Kamat
Syamimi, Shamsuddin
Electroencephalogram (EEG)-based systems to monitor driver fatigue: a review
description Link to publisher's homepage at http://ijneam.unimap.edu.my
author2 seri@utem.edu.my
author_facet seri@utem.edu.my
Seri Rahayu, Kamat
Muhammad Shafiq, Ibrahim
Seri Rahayu, Kamat
Syamimi, Shamsuddin
format Article
author Seri Rahayu, Kamat
Muhammad Shafiq, Ibrahim
Seri Rahayu, Kamat
Syamimi, Shamsuddin
author_sort Seri Rahayu, Kamat
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 (UniMAP)
publishDate 2022
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/76070
_version_ 1751537870578909184
score 13.214268

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