An Iterative Method for Classifying Stroke Subjects’ Motor Imagery EEG Data in the BCI-FES Rehabilitation Training System

  • Hao Zhang
  • Jianyi Liang
  • Ye Liu
  • Hang Wang
  • Liqing Zhang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)


Motor imagery-based BCI-FES rehabilitation system has been proved to be effective in the treatment of movement function recovery. Common Spatial Pattern (CSP) and Support Vector Machine (SVM) are commonly used in the feature extraction and classification of Two-classes motor imagery. However, motor imagery signals of stroke patients are irregular due to the damage of the specified brain area. Traditional CSP is not able to detect the optimal projection direction on such EEG data recorded from stroke patients under the interference of irregular patterns. In this paper, an adaptive CSP method is proposed to deal with these unknown irregular patterns. In the method, two models are trained and updated by using different subsets of the original data in every iteration procedure. The method is applied on the EEG datasets of several stroke subjects comparing with traditional CSP-SVM. The results also provide an evidence of the feasibility of our BCI-FES rehabilitation system.


EEG Stroke BCI-FES rehabilitation system Iteration Classification CSP SVM 



The work was supported by the National Natural Science Foundation of China (Grant No. 90920014 and 91120305) and the NSFC-JSPS International Cooperation Program (Grant No. 61111140019).


  1. 1.
    Sims N, Muyderman H (2010) Mitochondria, oxidative metabolism and cell death in stroke. Biochimica et Biophysica Acta (BBA)-Mol Basis Dis 1802(1):80–91Google Scholar
  2. 2.
    Cozean C, Pease W, Hubbell S (1988) Biofeedback and functional electric stimulation in stroke rehabilitation. Arch Phys Med Rehabil 69(6):401Google Scholar
  3. 3.
    Daly J, Wolpaw J (2008) Brain-computer interfaces in neurological rehabilitation. Lancet Neurol 7(11):1032–1043CrossRefGoogle Scholar
  4. 4.
    Li J, Zhang L, Tao D, Sun H, Zhao Q (2009) A prior neurophysiologic knowledge free tensor-based scheme for single trial EEG classification. IEEE Trans Neural Syst Rehabil Eng 17(2):107–115CrossRefGoogle Scholar
  5. 5.
    Ramoser H, Muller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial eeg during imagined hand movement. IEEE Trans Rehabil Eng 8(4):441–446CrossRefGoogle Scholar
  6. 6.
    Bishop C, En Ligne SS (2006) Pattern recognition and machine learning, Vol 4. Springer, New YorkGoogle Scholar
  7. 7.
    Chang C, Lin C (2011) Libsvm: a library for support vector machines. ACM Transon Intell Syst Technol 2(3):27Google Scholar
  8. 8.
    Neuper C, Müller G, Kübler A, Birbaumer N, Pfurtscheller G (2003) Clinical application of an eeg-based brain-computer interface: a case study in a patient with severe motor impairment. Clin Neurophysiol 114(3):399–409CrossRefGoogle Scholar
  9. 9.
    Li J, Zhang L (2012) Active training paradigm for motor imagery BCI. Exp Brain Res 219(2):245–254, SpringerGoogle Scholar
  10. 10.
    Li J, Zhang L (2010) Bilateral adaptation and neurofeedback for brain computer interface system. J Neurosci Methods 193(2):373–379CrossRefGoogle Scholar
  11. 11.
    Heidi S (2004) Motor rehabilitation using virtual reality. J Neuro Eng Rehabil 1:10. doi:  10.1186/1743-0003-1-10.
  12. 12.
    Zhao Q, Zhang L, Cichocki A (2009) Eeg-based asynchronous bci control of a car in 3d virtual reality environments. Chin Sci Bull 54(1):78–87CrossRefGoogle Scholar
  13. 13.
    Meng F, Tong K, Chan S, Wong W, Lui K, Tang K, Gao X, Gao S (2008) BCI-FES training system design and implementation for rehabilitation of stroke patients. In: IEEE international joint conference on neural networks IJCNN 2008 (IEEE world congress on computational intelligence), IEEE, pp 4103–4106Google Scholar
  14. 14.
    Wolf S, Winstein C, Miller J, Taub E, Uswatte G, Morris D, Giuliani C, Light K, Nichols-Larsen D et al (2006) Effect of constraint-induced movement therapy on upper extremity function 3–9 months after stroke. JAMA: J Am Med Assoc 296(17):2095–2104Google Scholar
  15. 15.
    Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning, Vol 1. Springer Series in StatisticsGoogle Scholar
  16. 16.
    Pfurtscheller G, Lopes da Silva F (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110(11):1842–1857Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Hao Zhang
    • 1
  • Jianyi Liang
    • 1
  • Ye Liu
    • 1
  • Hang Wang
    • 1
  • Liqing Zhang
    • 1
  1. 1.MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations