Abstract
BCI-FES therapy has been proved to be an effective way to help post-stroke patients restore motor function of paralyzed limbs. In the existing BCI-FES system, patients can only asynchronously receive feedback in the form of FES or robot-assisted arm movements, and such a system does not provide a positive feedback corresponding to patient's motor imagery. In this work, we propose a causal related BCI-FES rehabilitation training platform, consisting top-down and bottom-up causal chains to achieve a better rehabilitation performance. We compare our system with a popular BCI-FES system on EEG data recorded from ten patients divided in two groups. The results show that almost all patients have achieved improvements in the motor function recovery after our training. © Springer-Verlag 2013.
Cite
CITATION STYLE
Wang, H., Liu, Y., Zhang, H., Li, J., & Zhang, L. (2013). Causal neurofeedback based BCI-FES rehabilitation for post-stroke patients. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8226 LNCS, pp. 419–426). https://doi.org/10.1007/978-3-642-42054-2_52
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
