Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study
Machine learning algorithms showed enhanced movement phase classification during active tDCS in chronic stroke participants. These results suggest their feasibility for real-time movement detection in neurorehabilitation, including brain-computer interfaces for stroke recovery.
Authors
- Brian F. Saway (ORCID: https://orcid.org/0000-0002-7101-0981)
- M. S. Zobaer (ORCID: https://orcid.org/0000-0003-3764-2501)
- Parneet Grewal (ORCID: https://orcid.org/0000-0002-0638-4776)
- Matthew Triano (ORCID: https://orcid.org/0000-0002-8182-775X)
- Nathan C. Rowland (ORCID: https://orcid.org/0000-0002-6414-931X)
- Rishishankar Suresh (ORCID: https://orcid.org/0009-0005-8220-7949)
Institutions
- Medical University of South Carolina (US)
Publication Details
- Journal
- Brain Sciences
- Published
- 2024-12-29
- DOI
- https://doi.org/10.3390/brainsci15010028
- Citations
- 3
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.03