Predicting high-quality movements in post-stroke motor rehabilitation from EEG
- Author(s)
- Philipp Raggam, Christoph Zrenner, Eric J. McDermott, Ulf Ziemann, Moritz Grosse-Wentrup
- Abstract
A promising new concept for post-stroke motor rehabilitation is using EEG-based brain-computer interface (BCI) systems, e.g., providing patients with EEG-based feedback on their decoded movement intent. Here, we explore the possibility of extending BCI-based rehabilitation paradigms from decoding movement intent to decoding movement quality. Toward this goal, we study whether the quality of hand opening and closing movements in stroke patients with arm and hand spasticity can be decoded from their EEG.
- Organisation(s)
- Research Group Neuroinformatics, Vienna Cognitive Science Hub, Research Network Data Science
- External organisation(s)
- Eberhard Karls Universität Tübingen, University of Toronto, Scientific Working Group in Smoking Cessation (WAT) e.V., Department of Psychiatry and Psychotherapy, University Hospital Tübingen, 72076 Tübingen, Germany.
- Publication date
- 06-2023
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 301401 Brain research, 102001 Artificial intelligence
- Keywords
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/predicting-highquality-movements-in-poststroke-motor-rehabilitation-from-eeg(2cf919b1-fdfc-45e5-9e55-f586bef003a2).html