Calibration-Free Driver Drowsiness Classification With Prototype-Based Multi-Domain Mixup

Drowsy driving is one of the greatest threats to road safety, which increases the importance of intelligent systems that can monitor driver drowsiness. Electroencephalogram (EEG)–based monitoring systems have gained attention because EEG is known to directly measure brain activities that reflect the mental state of the driver. However, calibration is necessary before using the system because EEG signals vary between and within subjects. Therefore, generalized EEG-based drowsiness estimation has become challenging. In this paper, we propose an EEG-based driver drowsiness classification framework without the need for calibration, which can be generalized to unseen subjects. We augment the features of unseen domains (i.e., subjects) with a Dirichlet mixup between prototypes of source domains to complement other domain knowledge. The parameter $\boldsymbol {\alpha }$ vector of the Dirichlet distribution adjusts the intensity of the mixup, allowing for diverse enhancement. Furthermore, we utilize an auxiliary batch normalization module for augmented samples to avoid inaccurate estimation by the difference in distribution. The experiments were carried out using two EEG datasets, each measured using different drowsiness indicators, the Karolinska sleepiness scale, and reaction time. In leave-one-subject-out cross-validation, the proposed framework achieved outstanding performance in both datasets, an ${F}1$ -score of 62.69% and 70.33% and an area under the receiver operating characteristic curve (AUROC) of 71.73% and 73.80%, respectively. The experimental results demonstrate the potential for practical applications of brain-computer interfaces without calibration.