EEG-Based Cross-Dataset Driver Drowsiness Recognition With an Entropy Optimization Network

Cross-dataset driver drowsiness recognition with EEG is important for the advancement of a calibration-free driver drowsiness recognition system. Nevertheless, this task is challenging due to the impact of distribution drift on recognition accuracy. In this paper, we propose a novel model named entropy optimization network (EON) for the task. The model takes a novel two-step strategy to separate the unlabeled data from the target domain. It firstly uses a novel modified entropy loss to encourage unlabeled samples well aligned with the source domain to form clear clusters. Next, it gradually separates samples from the target domain with a self-training framework by taking adequate advantage of underlying patterns inherent in it. The proposed method is tested on the domain adaptation task with two public datasets and achieves 2-class recognition accuracies of $\boldsymbol{89.2\%}$ and $\boldsymbol{77.6\%}$, which beats other baseline methods. Our work illuminates a promising direction in achieving the ultimate objective of developing a driver drowsiness recognition system without calibration.