  Sleep stage classification is essential for sleep assessment and disease
diagnosis. Although previous attempts to classify sleep stages have achieved
high classification performance, several challenges remain open: 1) How to
effectively utilize time-varying spatial and temporal features from
multi-channel brain signals remains challenging. Prior works have not been able
to fully utilize the spatial topological information among brain regions. 2)
Due to the many differences found in individual biological signals, how to
overcome the differences of subjects and improve the generalization of deep
neural networks is important. 3) Most deep learning methods ignore the
interpretability of the model to the brain. To address the above challenges, we
propose a multi-view spatial-temporal graph convolutional networks (MSTGCN)
with domain generalization for sleep stage classification. Specifically, we
construct two brain view graphs for MSTGCN based on the functional connectivity
and physical distance proximity of the brain regions. The MSTGCN consists of
graph convolutions for extracting spatial features and temporal convolutions
for capturing the transition rules among sleep stages. In addition, attention
mechanism is employed for capturing the most relevant spatial-temporal
information for sleep stage classification. Finally, domain generalization and
MSTGCN are integrated into a unified framework to extract subject-invariant
sleep features. Experiments on two public datasets demonstrate that the
proposed model outperforms the state-of-the-art baselines.
