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
adaptive filters , driver fatigue detection , electroencephalogram spectrum , drowsiness detection , independent component analysis , fatigue‐related traffic accidents , electrooculography artefact removal , proposed ICA‐R algorithm , EEG spectrum‐related features , driver information systems , electrooculography artefacts removal , road safety , EEG spectrum feature extraction , adaptive filter , feature extraction , EEG power spectrum analysis , independent component analysis with reference , electroencephalography , support vector machines , support vector machine recursive feature elimination , detection system , driver fatigue monitoring
DRIVER ASSISTANCE SYSTEM AND METHOD USING ELECTROENCEPHALOGRAM
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