Deep learning-based EEG source imaging is robust under varying electrode configurations
Our findings suggest broad applications of the deep-learning based source imaging in clinical settings without the need for high-density EEG devices.
Authors
- Boney Joseph (ORCID: https://orcid.org/0000-0002-1576-9344)
- Rui Sun (ORCID: https://orcid.org/0000-0001-8426-940X)
- Gregory A. Worrell (ORCID: https://orcid.org/0000-0003-2916-0553)
- Bin He (ORCID: https://orcid.org/0000-0003-2944-8602)
- Jesse Rong
Institutions
- Mayo Clinic in Arizona (US)
- Carnegie Mellon University (US)
Publication Details
- Journal
- Clinical Neurophysiology
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1016/j.clinph.2025.04.009
- Citations
- 4
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 5.72
Funders
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke
- National Institute of Biomedical Imaging and Bioengineering