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Driver fatigue is a major cause of traffic accidents and theelectroencephalogram (EEG) is considered one of the most reliable predictors offatigue. This paper proposes a novel, simple and fast method for driver fatiguedetection that can be implemented in real-time systems by using asingle-channel on the scalp. The method based on the robust univariate analysisof EEG signals is composed of two stages. First, the most significant channelfrom EEG raw is selected according to the maximum variance. In the secondstage, this single channel will be used to detect the fatigue EEG signal byextracting four feature parameters. Two parameters estimated from the robustunivariate analysis, namely mean and covariance, and two classical statisticsparameters such as variance and covariance that help to tune the robustanalysis. Next, an ensemble bagged decision trees classifier is used in orderto discriminate fatigue signals from alert signals. The proposed algorithm isdemonstrated on 24 EEG signals from the Jiangxi University of Technologydatabase using only the most significant channel found, which is located in theleft tempo-parietal region where spatial awareness and visual-spatialnavigation are shared, in terms of 92.7% accuracy with 1.8 seconds of timedelay.

Driver fatigue EEG signals detection by using robust univariate analysis