# Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification

arXiv: https://arxiv.org/abs/2109.01824 · HTML: https://mlsanity.com/paper/2109.01824/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 4 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging](https://mlsanity.com/paper/1809.10932/) (2018)
- [A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series](https://mlsanity.com/paper/1707.03321/) (2017)
- [SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging](https://mlsanity.com/paper/2105.13864/) (2021)
- [Mixed Neural Network Approach for Temporal Sleep Stage Classification](https://mlsanity.com/paper/1610.06421/) (2016)
- [HetEmotionNet: Two-Stream Heterogeneous Graph Recurrent Neural Network for Multi-modal Emotion Recognition](https://mlsanity.com/paper/2108.03354/) (2021)
- [Detecting causality in multivariate time series via non-uniform embedding](https://mlsanity.com/paper/1903.05842/) (2019)

## Built on by (most cited first, 30 of 37)

- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Graph Neural Network-based EEG Classification: A Survey](https://mlsanity.com/paper/2310.02152/) (2023)
- [STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks](https://mlsanity.com/paper/2111.06750/) (2021)
- [OSF: On Pre-training and Scaling of Sleep Foundation Models](https://mlsanity.com/paper/2603.00190/) (2026)
- [DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI](https://mlsanity.com/paper/2601.06134/) (2026)
- [An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning](https://mlsanity.com/paper/2206.03420/) (2022)
- [Benchmarking Cross-Domain Audio-Visual Deception Detection](https://mlsanity.com/paper/2405.06995/) (2024)
- [Learning Product Graphs from Spectral Templates](https://mlsanity.com/paper/2211.02893/) (2022)
- [Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey](https://mlsanity.com/paper/2212.03176/) (2022)
- [ST-USleepNet: A Spatial-Temporal Coupling Prominence Network for Multi-Channel Sleep Staging](https://mlsanity.com/paper/2408.11884/) (2024)
- [Graph Generation Powered with LLMs for Boosting Multivariate Time-Series Representation Learning](https://mlsanity.com/paper/2403.03645/) (2024)
- [MSSC-BiMamba: Multimodal Sleep Stage Classification and Early Diagnosis of Sleep Disorders with Bidirectional Mamba](https://mlsanity.com/paper/2405.20142/) (2024)
- [A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications](https://mlsanity.com/paper/2410.08224/) (2024)
- [MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification](https://mlsanity.com/paper/2501.02949/) (2025)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning](https://mlsanity.com/paper/2309.02124/) (2023)
- [Continuously Evolving Graph Neural Controlled Differential Equations for Traffic Forecasting](https://mlsanity.com/paper/2401.14695/) (2024)
- [A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detection](https://mlsanity.com/paper/2410.18103/) (2024)
- [Seeing your sleep stage: cross-modal distillation from EEG to infrared video](https://mlsanity.com/paper/2208.05814/) (2022)
- [Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction](https://mlsanity.com/paper/2405.04336/) (2024)
- [SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment](https://mlsanity.com/paper/2501.03764/) (2025)
- [Quasi Zigzag Persistence: A Topological Framework for Analyzing Time-Varying Data](https://mlsanity.com/paper/2502.16049/) (2025)
- [FairSSL: Fair Multimodal Self-Supervised Learning](https://mlsanity.com/paper/2508.16748/) (2025)
- [Introducing Multimodal Paradigm for Learning Sleep Staging PSG via General-Purpose Model](https://mlsanity.com/paper/2509.22810/) (2025)
- [MEASURE: Multi-scale Minimal Sufficient Representation Learning for Domain Generalization in Sleep Staging](https://mlsanity.com/paper/2510.12070/) (2025)
- [Fine-gained air quality inference based on low-quality sensing data using self-supervised learning](https://mlsanity.com/paper/2408.09526/) (2024)
- [Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks](https://mlsanity.com/paper/2410.19819/) (2024)
- [MetaSTH-Sleep: Towards Effective Few-Shot Sleep Stage Classification for Health Management with Spatial-Temporal Hypergraph Enhanced Meta-Learning](https://mlsanity.com/paper/2505.17142/) (2025)
- [Spatial Imputation Drives Cross-Domain Alignment for EEG Classification](https://mlsanity.com/paper/2508.03437/) (2025)

## Abstract (arXiv)

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.