Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1819
Title: BCI-FES training system design and implementation for rehabilitation of stroke patients
Authors: Meng, F
Tong, KYR
Chan, STP
Wong, WW
Lui, KH
Tang, KW
Gao, X
Gao, S
Keywords: Brain-computer interfaces
Electroencephalography
Medical signal processing
Patient rehabilitation
Issue Date: 2008
Publisher: IEEE
Source: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2008 (IEEE World Congress on Computational Intelligence) : Hong Kong, China, June, 2008, p. 4103-4106 How to cite?
Abstract: A BCI-FES training platform has been designed for rehabilitation on chronic stroke patients to train their upper limb motor functions. The conventional functional electrical stimulation (FES) was driven by users' intention through EEG signals to move their wrist and hand. Such active participation was expected to be important for motor rehabilitation according to motor relearning theory. The common spatial pattern (CSP) algorithm was applied as one pre-processing step in brain-computer interface (BCI) module to search for the optimal spatial projection direction after brain reorganization. The pre- and post- clinical assessment was conducted to identify the possible functional improvement after the training. Two chronic stroke subjects attended this pilot study and the error rate of the BCI control was less than 20% after training of 10 sessions. This implementation showed the feasibility for stroke patients to accomplish the BCI triggered FES rehabilitation training.
URI: http://hdl.handle.net/10397/1819
ISBN: 978-1-4244-1821-3
Rights: © 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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