Optimal Electroencephalogram and Electrooculogram Signal Combination for Deep Learning-Based Sleep Staging
The comparable classification performances among various signal combinations suggest that automatic sleep staging can be achieved with a simplified EEG and EOG measurement setup without compromising performance.
Authors
- Sami Nikkonen (ORCID: https://orcid.org/0000-0003-0615-4118)
- Tuomas Karhu (ORCID: https://orcid.org/0000-0001-9464-447X)
- R. Huttunen (ORCID: https://orcid.org/0000-0001-8290-4764)
- Masoumeh Tashakori (ORCID: https://orcid.org/0000-0001-5117-3065)
- Matias Rusanen (ORCID: https://orcid.org/0000-0001-9633-6016)
- Timo Leppänen (ORCID: https://orcid.org/0000-0003-4017-821X)
Institutions
- Inserm (FR)
- University of Eastern Finland (FI)
- Université Grenoble Alpes (FR)
Publication Details
- Journal
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1109/jbhi.2025.3541453
- Citations
- 3
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 4.29