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Toward practical driving fatigue detection using three frontal EEG channels: A proof-of-concept study
Liu, Xucheng1,2; Li, Gang3,4; Wang, Sujie3; Wan, Feng1,2; Sun, Yi5; Wang, Hongtao6; Bezerianos, Anastasios7,8; Li, Chuantao9; Sun, Yu3,10
2021-04-01
Source PublicationPhysiological Measurement
ISSN0967-3334
Volume42Issue:4
Abstract

Objective. Although various driving fatigue detection strategies have been introduced, the limited practicability is still an obstacle for the real application of these technologies. This study is based on the newly proposed non-hair-bearing (NHB) method to achieve practical driving fatigue detection with fewer channels from NHB areas and more efficient electroencephalogram (EEG) features. Approach. EEG data were recorded from 20 healthy subjects (15 males, age = 22.2 ± 3.2 years) in a 90 min simulated driving task using a remote wireless cap. Behaviorally, subjects demonstrated a salient fatigue effect, as reflected by a monotonic increase in reaction time. Using a sliding-window approach, we determined the vigilant and fatigued states at individual level to reduce the inter-subject differences in behavioral impairment and brain activity. Multiple EEG features, including power-spectrum density (PSD), functional connectivity (FC), and entropy, were estimated in a pairwise manner, which were set as input for fatigue classification. Main results. Intriguingly, this data-driven approach showed that the best classification performance was achieved using three EEG channel pairs located in the NHB area. The mixed features of the frontal NHB area lead to the high within-subject detection rate of driving fatigue (92.7% ± 0.92%) with satisfactory generalizability for fatigue classification across different subjects (77.13% ± 0.85%). Moreover, we found the most prominent contributing features were PSD of different frequency bands within the frontal NHB area and FC within the frontal NHB area and between frontal and parietal areas. Significance. In summary, the current work provided objective evidence to support the effectiveness of the NHB method and further improved the performance, thereby moving a step forward towards practical driving fatigue detection in real-world scenarios.

KeywordDriving Fatigue Electroencephalogram (Eeg) Feature Selection Functional Connectivity Non-hair-bearing (Nhb)
DOI10.1088/1361-6579/abf336
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaBiophysics ; Engineering ; Physiology
WOS SubjectBiophysics ; Engineering, Biomedical ; Physiology
WOS IDWOS:000655296900001
Scopus ID2-s2.0-85106558623
Fulltext Access
FWCI2.486972
Citation statistics
Cited Times [WOS]:10   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Corresponding AuthorLi, Chuantao
Affiliation1.Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macao
2.Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macao
3.Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Zhejiang, China
4.College of Engineering, Zhejiang Normal University, Zhejiang, China
5.Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang, China
6.Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China
7.N1 Institute for Health, National University of Singapore, Singapore
8.Hellenic Institute of Transportation, Centre for Research and Technology Hellas, Thessaloniki, Greece
9.Naval Medical Center of PLA, Department of Aviation Medicine, Naval Military Medical University, Shanghai, China
10.Zhejiang Lab, Zhejiang, China
First Author AffilicationFaculty of Science and Technology;  INSTITUTE OF COLLABORATIVE INNOVATION
Recommended Citation
GB/T 7714
Liu, Xucheng,Li, Gang,Wang, Sujie,et al. Toward practical driving fatigue detection using three frontal EEG channels: A proof-of-concept study[J]. Physiological Measurement,2021,42(4).
APA Liu, Xucheng,Li, Gang,Wang, Sujie,Wan, Feng,Sun, Yi,Wang, Hongtao,Bezerianos, Anastasios,Li, Chuantao,&Sun, Yu.(2021).Toward practical driving fatigue detection using three frontal EEG channels: A proof-of-concept study.Physiological Measurement,42(4).
MLA Liu, Xucheng,et al."Toward practical driving fatigue detection using three frontal EEG channels: A proof-of-concept study".Physiological Measurement 42.4(2021).
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