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Driving Fatigue Detection from EEG Using a Modified PCANet Method
Joint Authors
She, Qingshan
Ma, Yuliang
Luo, Zhizeng
Zhang, Yingchun
Chen, Bin
Li, Rihui
Wang, Chushan
Wang, Jun
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-14
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents.
A large proportion of traffic accidents were caused by driving fatigue.
EEG is considered as a direct, effective, and promising modality to detect driving fatigue.
In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection.
EEG signals were recorded from six healthy volunteers in a simulated driving experiment.
The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet).
In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection.
Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field.
We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue.
This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection.
American Psychological Association (APA)
Ma, Yuliang& Chen, Bin& Li, Rihui& Wang, Chushan& Wang, Jun& She, Qingshan…[et al.]. 2019. Driving Fatigue Detection from EEG Using a Modified PCANet Method. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1129465
Modern Language Association (MLA)
Ma, Yuliang…[et al.]. Driving Fatigue Detection from EEG Using a Modified PCANet Method. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1129465
American Medical Association (AMA)
Ma, Yuliang& Chen, Bin& Li, Rihui& Wang, Chushan& Wang, Jun& She, Qingshan…[et al.]. Driving Fatigue Detection from EEG Using a Modified PCANet Method. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1129465
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129465