NDLI: Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal

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Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal

Content Provider	IET Digital Library
Author	Hu, Shuyan Zheng, Gangtie Peters, Björn
Abstract	A driver fatigue monitoring and detection system with high accuracy could be a valuable countermeasure to decrease fatigue-related traffic accidents. This study proposes methods for drowsiness detection based on electroencephalogram (EEG) power spectrum analysis. First, a new algorithm is proposed for independent component analysis with reference (ICA-R) for electrooculography artefacts removal. Comparison is then carried out between the proposed ICA-R algorithm and an adaptive filter. Secondly, 75 EEG spectrum features are extracted from the cleaned EEG. Among all the EEG spectrum-related features, 40 key features are selected by support vector machine recursive feature elimination to improve the performance of the classifier. The validation results show that 86% of the driver's drowsiness states can be accurately detected among drivers, who participate a driving simulator study.
Starting Page	105
Ending Page	113
Page Count	9
ISSN	1751956X
Volume Number	7
e-ISSN	17519578
Issue Number	Issue 1, Mar (2013)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/7/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2012.0045
Journal	IET Intelligent Transport Systems
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Filter Bioelectric Signal Detection System Digital Signal Processing Driver Fatigue Detection Driver Fatigue Monitoring Driver Information System Drowsiness Detection EEG Power Spectrum Analysis EEG Spectrum Feature Extraction EEG Spectrum-related Features Electroencephalogram Spectrum Electroencephalography Electrooculography Artifact Removal Electrooculography Artifacts Removal Fatigue-related Traffic Accidents Feature Extraction Filtering Method in Signal Processing Independent Component Analysis Independent Component Analysis with Reference Knowledge Engineering Technique Proposed ICA-R Algorithm Road Safety Statistics Support Vector Machine Support Vector Machine Recursive Feature Elimination Traffic Engineering Computing
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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