  Driver drowsiness is one of main factors leading to road fatalities and
hazards in the transportation industry. Electroencephalography (EEG) has been
considered as one of the best physiological signals to detect drivers drowsy
states, since it directly measures neurophysiological activities in the brain.
However, designing a calibration-free system for driver drowsiness detection
with EEG is still a challenging task, as EEG suffers from serious mental and
physical drifts across different subjects. In this paper, we propose a compact
and interpretable Convolutional Neural Network (CNN) to discover shared EEG
features across different subjects for driver drowsiness detection. We
incorporate the Global Average Pooling (GAP) layer in the model structure,
allowing the Class Activation Map (CAM) method to be used for localizing
regions of the input signal that contribute most for classification. Results
show that the proposed model can achieve an average accuracy of 73.22% on 11
subjects for 2-class cross-subject EEG signal classification, which is higher
than conventional machine learning methods and other state-of-art deep learning
methods. It is revealed by the visualization technique that the model has
learned biologically explainable features, e.g., Alpha spindles and Theta
burst, as evidence for the drowsy state. It is also interesting to see that the
model uses artifacts that usually dominate the wakeful EEG, e.g., muscle
artifacts and sensor drifts, to recognize the alert state. The proposed model
illustrates a potential direction to use CNN models as a powerful tool to
discover shared features related to different mental states across different
subjects from EEG signals.
