Sleep stage classification has important clinical significance for the diagnosis of sleep-related diseases. To pursue more accurate sleep stage classification, multi-channel sleep signals are widely used due to the rich spatial-temporal information contained. However, it leads to a great increment in the size and computational costs, which constrain the application of multi-channel sleep models on hardware devices. Knowledge distillation is an effective way to compress models, yet existing knowledge distillation methods cannot fully extract and transfer the spatial-temporal knowledge in the multi-channel sleep signals. To solve the problem, we propose a general knowledge distillation framework for multi-channel sleep stage classification called spatial-temporal mutual distillation. Based on the spatial relationship of human body and the temporal transition rules of sleep signals, the spatial and temporal modules are designed to extract the spatial-temporal knowledge, thus help the lightweight student model learn the rich spatial-temporal knowledge from large-scale teacher model. The mutual distillation framework transfers the spatial-temporal knowledge mutually. Teacher model and student model can learn from each other, further improving the student model. The results on the ISRUC-III and MASS-SS3 datasets show that our proposed framework compresses the sleep models effectively with minimal performance loss and achieves the state-of-the-art performance compared to the baseline methods.

Sleep stage classification is crucial for sleep state monitoring and health interventions. In accordance with the standards prescribed by the American Academy of Sleep Medicine, a sleep episode follows a specific structure comprising five distinctive sleep stages that collectively form a sleep cycle. Typically, this cycle repeats about five times, providing an insightful portrayal of the subject’s physiological attributes. The progress of deep learning and advanced domain generalization methods allows automatic and even adaptive sleep stage classification. However, applying models trained with visible subject data to invisible subject data remains challenging due to significant individual differences among subjects. Motivated by the periodic category-complete structure of sleep stage classification, we propose a Structure Incentive Domain Adversarial learning (SIDA) method that combines the sleep stage classification method with domain generalization to enable cross-subject sleep stage classification. SIDA includes individual domain discriminators for each sleep stage category to decouple subject dependence differences among different categories and fine-grained learning of domain-invariant features. Furthermore, SIDA directly connects the label classifier and domain discriminators to promote the training process. Experiments on three benchmark sleep stage classification datasets demonstrate that the proposed SIDA method outperforms other state-of-the-art sleep stage classification and domain generalization methods and achieves the best cross-subject sleep stage classification results.

Motor imagery (MI), as one of the important applications of brain-computer interface (BCI), has lately received great attention. However, current MI researches have not provided satisfactory representations of electroencephalogram (EEG), taking account of the space-time-frequency features for MI classification. Moreover, those models also lack the exploration of attentive spatial, temporal, and spectral dynamics. In this study, we propose TA3D (Two-stream Attention based 3D network), a novel model for MI classification. It mainly consists of two streams: the space-time stream and the space-frequency stream, representing and learning discriminative features in the space-time-frequency dimension. Specifically, each stream contains three key parts: 1) 3D representations of EEG signals depict the spatial information over temporal/spectral distributions; 2) Attention mechanisms adaptively explore attentive dynamics of EEG signals and focus on the most valuable information in separate dimensions; 3) 3D convolutions learn spatial representation, temporal dependence, and spectral dependence. The outputs of the two streams are concatenated for space-time-frequency feature fusion. Extensive experiments implemented on two BCI datasets demonstrate that our model outperforms state-of-the-art MI classification methods.