An Attention-UNet Model to Remove Artifacts from Single-Chan…
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- DOI 10.1109/cisp-bmei64163.2024.10906238
- OpenAlex ID W4408200520
- Type Conference-paper
- Language EN
- Published 26 Oct 2024
- Pages 1–6
- Source International Congress on Image and Sign…
- FWCI 3.14
- Countries 1
- Institutions 1
- Added 07 Mar 2025
- Updated 26 Oct 2026
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An Attention-UNet Model to Remove Artifacts from Single-Channel EEG
Conference-paper 2024 EN
Electroencephalography (EEG) signals are often unavoidably contaminated by physiological artifacts during acquisition. Reducing the effects of these artifacts is important for various EEG-based interpretation tasks. However, the existing deep learning EEG denoising methods currently have some shortcomings: information lost caused by multiple downsampling and upsampling, artifact-related regions no…
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2 authors| # | Author | Institutions | ORCID |
|---|---|---|---|
| 1 | Suo Chen | East China University of Scien… (CN) | — |
| 2 | Wang Bei | East China University of Scien… (CN) |
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