  Driver fatigue is a major cause of traffic accidents and the
electroencephalogram (EEG) is considered one of the most reliable predictors of
fatigue. This paper proposes a novel, simple and fast method for driver fatigue
detection that can be implemented in real-time systems by using a
single-channel on the scalp. The method based on the robust univariate analysis
of EEG signals is composed of two stages. First, the most significant channel
from EEG raw is selected according to the maximum variance. In the second
stage, this single channel will be used to detect the fatigue EEG signal by
extracting four feature parameters. Two parameters estimated from the robust
univariate analysis, namely mean and covariance, and two classical statistics
parameters such as variance and covariance that help to tune the robust
analysis. Next, an ensemble bagged decision trees classifier is used in order
to discriminate fatigue signals from alert signals. The proposed algorithm is
demonstrated on 24 EEG signals from the Jiangxi University of Technology
database using only the most significant channel found, which is located in the
left tempo-parietal region where spatial awareness and visual-spatial
navigation are shared, in terms of 92.7% accuracy with 1.8 seconds of time
delay.
