Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal

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.