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Enhancement of morlet mother wavelet in time–frequency domain in electroencephalogram (EEG) signals for driver fatigue classification

Driving is hazardous due to various factors, including driving attitudes, road type, and driving perceptual environment. These influences factors may cause a fatigue condition. Moreover, less driving experience and lack of alertness can also be contributed to dangerous accidents. Fatigued driving is...

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Main Authors: Rafiuddin, Abdubrani, Mahfuzah, Mustafa, Zarith Liyana, Zahari
格式: Conference or Workshop Item
语言:English
出版: Springer 2023
主题:
TK Electrical engineering. Electronics Nuclear engineering
在线阅读:http://umpir.ump.edu.my/id/eprint/43650/1/Enhancement%20of%20Morlet%20Mother%20Wavelet%20in%20Time%E2%80%93Frequency%20Domain%20in%20Electroencephalogram%20%28EEG%29%20Signals%20for%20Driver%20Fatigue%20Classification.PDF
http://umpir.ump.edu.my/id/eprint/43650/
https://doi.org/10.1007/978-981-19-8703-8_13
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spelling my.ump.umpir.436502025-01-24T01:10:05Z http://umpir.ump.edu.my/id/eprint/43650/ Enhancement of morlet mother wavelet in time–frequency domain in electroencephalogram (EEG) signals for driver fatigue classification Rafiuddin, Abdubrani Mahfuzah, Mustafa Zarith Liyana, Zahari TK Electrical engineering. Electronics Nuclear engineering Driving is hazardous due to various factors, including driving attitudes, road type, and driving perceptual environment. These influences factors may cause a fatigue condition. Moreover, less driving experience and lack of alertness can also be contributed to dangerous accidents. Fatigued driving is a key factor in car accidents worldwide because of sleep disorders and driving durations. An EEG signal is used to determine changes in brain activity for diagnosing driver fatigue states. Artifacts were removed using independent component analysis (ICA) in the preprocessing stage. Then, features are extracted from the temporal region of the brain using eight channels (Fp1, Fp2, O1, O2, F4, F3, P4, and P3). The frequency bands used are alpha, delta, and theta. In continuous wavelet transform analysis, the Morlet wavelet is a fast wavelet transform in time–frequency analysis. Still, it has shift sensitivity and lacks phase information, affecting the frequency resolution analysis. This study proposes the enhancement of the Morlet mother wavelet for frequency resolution in the time–frequency domain using independent component analysis to overcome the drawbacks of the Morlet wavelet. The proposed technique can increase the percentage of driver fatigue classification accuracy of EEG signals. Then, the artificial neural network (ANN) classifier with Levenberg–Marquardt (LM) training algorithm gives the highest accuracy of the classification results with 97.40%, followed by the k-nearest neighbor (KNN) with 95.83% and the support vector machine (SVM) with 83%. Springer 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/43650/1/Enhancement%20of%20Morlet%20Mother%20Wavelet%20in%20Time%E2%80%93Frequency%20Domain%20in%20Electroencephalogram%20%28EEG%29%20Signals%20for%20Driver%20Fatigue%20Classification.PDF Rafiuddin, Abdubrani and Mahfuzah, Mustafa and Zarith Liyana, Zahari (2023) Enhancement of morlet mother wavelet in time–frequency domain in electroencephalogram (EEG) signals for driver fatigue classification. In: Advances in Intelligent Manufacturing and Mechatronics, Lecture Notes in Electrical Engineering. Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2022 , 20 July 2022 , Pekan, Pahang. pp. 151-161., 988. ISSN 1876-1100 ISBN 978-981198702-1 (Published) https://doi.org/10.1007/978-981-19-8703-8_13
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Rafiuddin, Abdubrani
Mahfuzah, Mustafa
Zarith Liyana, Zahari
Enhancement of morlet mother wavelet in time–frequency domain in electroencephalogram (EEG) signals for driver fatigue classification
description Driving is hazardous due to various factors, including driving attitudes, road type, and driving perceptual environment. These influences factors may cause a fatigue condition. Moreover, less driving experience and lack of alertness can also be contributed to dangerous accidents. Fatigued driving is a key factor in car accidents worldwide because of sleep disorders and driving durations. An EEG signal is used to determine changes in brain activity for diagnosing driver fatigue states. Artifacts were removed using independent component analysis (ICA) in the preprocessing stage. Then, features are extracted from the temporal region of the brain using eight channels (Fp1, Fp2, O1, O2, F4, F3, P4, and P3). The frequency bands used are alpha, delta, and theta. In continuous wavelet transform analysis, the Morlet wavelet is a fast wavelet transform in time–frequency analysis. Still, it has shift sensitivity and lacks phase information, affecting the frequency resolution analysis. This study proposes the enhancement of the Morlet mother wavelet for frequency resolution in the time–frequency domain using independent component analysis to overcome the drawbacks of the Morlet wavelet. The proposed technique can increase the percentage of driver fatigue classification accuracy of EEG signals. Then, the artificial neural network (ANN) classifier with Levenberg–Marquardt (LM) training algorithm gives the highest accuracy of the classification results with 97.40%, followed by the k-nearest neighbor (KNN) with 95.83% and the support vector machine (SVM) with 83%.
format Conference or Workshop Item
author Rafiuddin, Abdubrani
Mahfuzah, Mustafa
Zarith Liyana, Zahari
author_facet Rafiuddin, Abdubrani
Mahfuzah, Mustafa
Zarith Liyana, Zahari
author_sort Rafiuddin, Abdubrani
title Enhancement of morlet mother wavelet in time–frequency domain in electroencephalogram (EEG) signals for driver fatigue classification
title_short Enhancement of morlet mother wavelet in time–frequency domain in electroencephalogram (EEG) signals for driver fatigue classification
title_full Enhancement of morlet mother wavelet in time–frequency domain in electroencephalogram (EEG) signals for driver fatigue classification
title_fullStr Enhancement of morlet mother wavelet in time–frequency domain in electroencephalogram (EEG) signals for driver fatigue classification
title_full_unstemmed Enhancement of morlet mother wavelet in time–frequency domain in electroencephalogram (EEG) signals for driver fatigue classification
title_sort enhancement of morlet mother wavelet in time–frequency domain in electroencephalogram (eeg) signals for driver fatigue classification
publisher Springer
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/43650/1/Enhancement%20of%20Morlet%20Mother%20Wavelet%20in%20Time%E2%80%93Frequency%20Domain%20in%20Electroencephalogram%20%28EEG%29%20Signals%20for%20Driver%20Fatigue%20Classification.PDF
http://umpir.ump.edu.my/id/eprint/43650/
https://doi.org/10.1007/978-981-19-8703-8_13
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score 13.251813

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