SleepMFormer: An Efficient Attention Framework with Contrastive Learning for Single-Channel EEG Sleep Staging
Overall, SleepMFormer introduces an efficient and practical framework for automatic sleep staging, demonstrating strong potential for related clinical applications.
Authors
- Hy Jin
- Pan Jl
- Xiaoying Zhang (ORCID: https://orcid.org/0000-0002-0055-7322)
- Shurong Dong
- Jie Xia (ORCID: https://orcid.org/0009-0005-5688-1649)
- Sha Zhao
Institutions
- Zhejiang Lab (CN)
- Hangzhou Seventh Peoples Hospital (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Brain Sciences
- Published
- 2026-01-16
- DOI
- https://doi.org/10.3390/brainsci16010095
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Government of Jiangsu Province
- Major Research Plan