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)

Abstract

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.

Keywords

EEG Stroke BCI-FES rehabilitation system Iteration Classification CSP SVM 

Notes

Acknowledgments

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).

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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

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