Classification of Movement-Related Potentials for Brain-Computer Interface: A Reinforcement Training Approach

  • Zongtan Zhou
  • Yang Liu
  • Dewen Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


This paper presents a new data driven approach to enhance linear classifier design, where the classifier obtained through theoretical model, is optimized through reinforcement training, to fit the data better as well as to improve its generalization ability. Applied to motor imagery experiment data in EEG based Brain-computer interface (BCI) applications, this method achieved a rather lower mean squared error of 0.59, and by which our group got a second place in the BCI competition III (dataset IVc).


Mean Square Error Motor Imagery Virtual Channel Linear Discriminant Function Reinforcement Training 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zongtan Zhou
    • 1
  • Yang Liu
    • 1
  • Dewen Hu
    • 1
  1. 1.Department of Automatic Control, College of Mechatronics and AutomationNational University of Defense TechnologyChangshaP.R. China

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