  Drowsy driving has a crucial influence on driving safety, creating an urgent
demand for driver drowsiness detection. Electroencephalogram (EEG) signal can
accurately reflect the mental fatigue state and thus has been widely studied in
drowsiness monitoring. However, the raw EEG data is inherently noisy and
redundant, which is neglected by existing works that just use single-channel
EEG data or full-head channel EEG data for model training, resulting in limited
performance of driver drowsiness detection. In this paper, we are the first to
propose an Interpretability-guided Channel Selection (ICS) framework for the
driver drowsiness detection task. Specifically, we design a two-stage training
strategy to progressively select the key contributing channels with the
guidance of interpretability. We first train a teacher network in the first
stage using full-head channel EEG data. Then we apply the class activation
mapping (CAM) to the trained teacher model to highlight the high-contributing
EEG channels and further propose a channel voting scheme to select the top N
contributing EEG channels. Finally, we train a student network with the
selected channels of EEG data in the second stage for driver drowsiness
detection. Experiments are designed on a public dataset, and the results
demonstrate that our method is highly applicable and can significantly improve
the performance of cross-subject driver drowsiness detection.
