Mitigating inter-subject brain signal variability for EEG-based driver fatigue state classification

With great research advances on Brain-Computer-Interface (BCI) systems, Electroencephalography (EEG) based driver fatigue state classification models have shown its effectiveness. However, EEG signals contain large differences between individuals, making it hard to build a unified model among individuals. In this paper, we propose a subject-independent EEG-based driver fatigue state (i.e., awake, tired, and drowsy) classification model that mitigates a performance gap between subjects. To this end, we exploit an adversarial training strategy to make our classification model misclassify the subject labels. Besides, we propose an Inter-subject Feature Distance Minimization (IFDM) method that minimizes the Wasserstein distance between two different subject groups of the same class to reduce the individual performance discrepancy. Our method is also designed to enable training even if the subject labels are not sufficiently included in the EEG dataset. To demonstrate the ability of the proposed method, we conduct a drowsiness classification task on a publicly available SEED-VIG dataset. The experimental results show our model achieves the highest accuracy and the lowest individual performance variability.