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.
Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal
IET Intelligent Transport Systems ; 7 , 1 ; 105-113
2013-03-01
9 pages
Article (Journal)
Electronic Resource
English
proposed ICA-R algorithm , support vector machine recursive feature elimination , road safety , detection system , driver fatigue monitoring , independent component analysis , adaptive filters , electroencephalogram spectrum , EEG power spectrum analysis , driver fatigue detection , support vector machines , electrooculography artefacts removal , independent component analysis with reference , fatigue-related traffic accidents , EEG spectrum feature extraction , EEG spectrum-related features , feature extraction , electrooculography artefact removal , drowsiness detection , electroencephalography , driver information systems , adaptive filter
Metadata by IET is licensed under CC BY 3.0
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