• DocumentCode
    2964042
  • Title

    BCI-FES training system design and implementation for rehabilitation of stroke patients

  • Author

    Meng, Fei ; Tong, Kai-yu ; Chan, Suk-tak ; Wong, Wan-wa ; Lui, Ka-him ; Tang, Kwok-wing ; Gao, Xiaorong ; Gao, Shangkai

  • Author_Institution
    Dept. of Biomed. Eng., Tsinghua Univ., Beijing
  • fYear
    2008
  • fDate
    1-8 June 2008
  • Firstpage
    4103
  • Lastpage
    4106
  • 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 userspsila 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.
  • Keywords
    brain-computer interfaces; electroencephalography; medical signal processing; patient rehabilitation; EEG signals motor relearning theory; brain reorganization; brain-computer interface; chronic stroke patients rehabilitation; common spatial pattern algorithm; functional electrical stimulation; optimal spatial projection direction; upper limb motor functions; Biomedical engineering; Biomedical imaging; Biomedical informatics; Brain computer interfaces; Electrodes; Electroencephalography; Feedback; Muscles; Neuromuscular stimulation; Wrist;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1820-6
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2008.4634388
  • Filename
    4634388