Modified Feature Selection for Improved Classification of Resting-State Raw EEG Signals in Chronic Knee Pain
We have shown that an automatic approach can be employed to find a compact connectivity feature set that effectively predicts chronic knee pain from EEG. It may shed light on the research of chronic pains and lead to future clinical solutions for diagnosis and treatment.
Authors
- Ramakrishnan Mani (ORCID: https://orcid.org/0000-0003-1255-5209)
- Jeremiah D. Deng (ORCID: https://orcid.org/0000-0003-3727-4403)
- Jean Li (ORCID: https://orcid.org/0000-0002-5545-8102)
- Divya Bharatkumar Adhia (ORCID: https://orcid.org/0000-0002-2505-4916)
- Matthew J. Hall (ORCID: https://orcid.org/0000-0003-3931-689X)
- Dirk De Ridder
Institutions
- University of Otago (NZ)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2025-02-07
- DOI
- https://doi.org/10.1109/tbme.2024.3517659
- Citations
- 1
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.43