  Driver Drowsiness is one of the leading causes of road accidents.
Electroencephalography (EEG) is highly affected by drowsiness; hence, EEG-based
methods detect drowsiness with the highest accuracy. Developments in
manufacturing dry electrodes and headsets have made recording EEG more
convenient. Vehicle-based features used for detecting drowsiness are easy to
capture but do not have the best performance. In this paper, we investigated
the performance of EEG signals recorded in 4 channels with commercial headsets
against the vehicle-based technique in drowsiness detection. We recorded EEG
signals of 50 volunteers driving a simulator in drowsy and alert states by
commercial devices. The observer rating of the drowsiness method was used to
determine the drowsiness level of the subjects. The meaningful separation of
vehicle-based features, recorded by the simulator, and EEG-based features of
the two states of drowsiness and alertness have been investigated. The
comparison results indicated that the EEG-based features are separated with
lower p-values than the vehicle-based ones in the two states. It is concluded
that EEG headsets can be feasible alternatives with better performance compared
to vehicle-based methods for detecting drowsiness.
