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E-Key: an EEG-Based Biometric Authentication and Driving Fatigue Detection System
Xu, Tao1; Wang, Hongtao2; Lu, Guanyong3; Wan, Feng4; Deng, Mengqi5; Qi, Peng6; Bezerianos, Anastasios7; Guan, Cuntai8; Sun, Yu9
2021
Source PublicationIEEE Transactions on Affective Computing
Abstract

Due to the increasing fatal traffic accidents, there are strong desire for more effective and convenient techniques for driving fatigue detection. Here, we propose a unified framework E-Key to simultaneously perform personal identification (PI) and driving fatigue detection using a convolutional attention neural network (CNN-Attention). The performance was assessed using EEG data collected through a wearable dry-sensor system from 31 healthy subjects undergoing a 90-min simulated driving task. In comparison with three widely-used competitive models (including CNN, CNN-LSTM, and Attention), the proposed scheme achieved the best (p < 0.01) performance in both PI (98.5%) and fatigue detection (97.8%). Besides, the spatial-temporal structure of the proposed framework exhibits an optimal balance between classification performance and computational efficiency. Additional validation analyses were conducted to assess the reliability and practicability of the model via re-configuring the kernel size and manipulating the input data, showing that it can achieve a satisfactory performance using a subset of the input data. In sum, these findings would pave the way for further practical implementation of in-vehicle expert system, showing great potential in autonomous driving and car-sharing where currently monitoring of PI and driving fatigue are of particular interest.

KeywordAuthentication Biometric Brain Modeling Brakes Convolutional Neural Network Convolutional Neural Networks Data Models Driving Fatigue Eeg Electroencephalography Fatigue Feature Extraction
DOI10.1109/TAFFC.2021.3133443
URLView the original
Language英語English
Scopus ID2-s2.0-85121356791
Fulltext Access
FWCI0.0
Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Affiliation1.Faculty of Intelligent Manufacturing, Wuyi University, 47892 Jiangmen, Guangdong, China, (e-mail: wanxiao0756@gmail.com)
2.Faculty of Intelligent Manufacturing, Wuyi University, 47892 Jiangmen, Guangdong, China, (e-mail: hongtaowang@mail.wyu.edu.cn)
3.Faculty of Intelligent Manufacturing, Wuyi University, 47892 Jiangmen, Guangdong, China, (e-mail: 13630443901@163.com)
4.Department of Electrical and Computer Engineering, University of Macau, 59193 Taipa, N.A., Macao, N.A. (e-mail: fwan@um.edu.mo)
5.Department of Electromechanical Engineering, University of Macau Faculty of Science and Technology, 365328 Taipa, Macau, China, (e-mail: yb87464@umac.mo)
6.Department of Control Science and Engineering, Tongji University, 12476 Shanghai, Shanghai, China, (e-mail: pqi@tongji.edu.cn)
7.Centre for Life Science, National University of Singapore, 37580 Singapore, Singapore, Singapore, (e-mail: tassos.bezerianos@nus.edu.sg)
8.School of Computer Science and Engineering, Nanyang Technological University, 54761 Singapore, North West, Singapore, 639798 (e-mail: ctguan@ntu.edu.sg)
9.Biomedical Engineering, Zhejiang University, 12377 Hangzhou, Zhejiang, China, 310058 (e-mail: yusun@zju.edu.cn)
Recommended Citation
GB/T 7714
Xu, Tao,Wang, Hongtao,Lu, Guanyong,et al. E-Key: an EEG-Based Biometric Authentication and Driving Fatigue Detection System[J]. IEEE Transactions on Affective Computing,2021.
APA Xu, Tao,Wang, Hongtao,Lu, Guanyong,Wan, Feng,Deng, Mengqi,Qi, Peng,Bezerianos, Anastasios,Guan, Cuntai,&Sun, Yu.(2021).E-Key: an EEG-Based Biometric Authentication and Driving Fatigue Detection System.IEEE Transactions on Affective Computing.
MLA Xu, Tao,et al."E-Key: an EEG-Based Biometric Authentication and Driving Fatigue Detection System".IEEE Transactions on Affective Computing (2021).
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