# EEG-based Sleep Staging with Hybrid Attention

arXiv: https://arxiv.org/abs/2305.09543 · HTML: https://mlsanity.com/paper/2305.09543/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 6 of 6)

- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [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)
- [U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging](https://mlsanity.com/paper/1910.11162/) (2019)
- [A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series](https://mlsanity.com/paper/1707.03321/) (2017)
- [Interpretable and Robust AI in EEG Systems: A Survey](https://mlsanity.com/paper/2304.10755/) (2023)

## Built on by (most cited first, 1 of 1)

- [SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment](https://mlsanity.com/paper/2501.03764/) (2025)

## Abstract (arXiv)

Sleep staging is critical for assessing sleep quality and diagnosing sleep disorders. However, capturing both the spatial and temporal relationships within electroencephalogram (EEG) signals during different sleep stages remains challenging. In this paper, we propose a novel framework called the Hybrid Attention EEG Sleep Staging (HASS) Framework. Specifically, we propose a well-designed spatio-temporal attention mechanism to adaptively assign weights to inter-channels and intra-channel EEG segments based on the spatio-temporal relationship of the brain during different sleep stages. Experiment results on the MASS and ISRUC datasets demonstrate that HASS can significantly improve typical sleep staging networks. Our proposed framework alleviates the difficulties of capturing the spatial-temporal relationship of EEG signals during sleep staging and holds promise for improving the accuracy and reliability of sleep assessment in both clinical and research settings.