A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems
Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve succe...
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doaj-e24a61f1111c4453b54be8e7477520ba2021-06-01T01:38:54ZengMDPI AGSensors1424-82202021-05-01213786378610.3390/s21113786A Review of EEG Signal Features and their Application in Driver Drowsiness Detection SystemsIgor Stancin0Mario Cifrek1Alan Jovic2Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, CroatiaDetecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.https://www.mdpi.com/1424-8220/21/11/3786drowsiness detectionEEG featuresfeature extractionmachine learningdrowsiness classificationfatigue detection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Igor Stancin Mario Cifrek Alan Jovic |
spellingShingle |
Igor Stancin Mario Cifrek Alan Jovic A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems Sensors drowsiness detection EEG features feature extraction machine learning drowsiness classification fatigue detection |
author_facet |
Igor Stancin Mario Cifrek Alan Jovic |
author_sort |
Igor Stancin |
title |
A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems |
title_short |
A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems |
title_full |
A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems |
title_fullStr |
A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems |
title_full_unstemmed |
A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems |
title_sort |
review of eeg signal features and their application in driver drowsiness detection systems |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-05-01 |
description |
Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection. |
topic |
drowsiness detection EEG features feature extraction machine learning drowsiness classification fatigue detection |
url |
https://www.mdpi.com/1424-8220/21/11/3786 |
work_keys_str_mv |
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1721411954785910784 |