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A New Hybrid Method with Biomimetic Pattern Recognition and Sparse Representation for EEG Classification

  • Yanbin Ge
  • Yan Wu
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)

Abstract

This paper presents a novel classification framework combining Biomimetic Pattern Recognition (BPR) with Sparse Representation (SR) for Brain Computer Interface based on motor imagery. This framework can work well when encountering the overlap coverage problem of BPR by introducing the idea of SR. Using Common Spatial Pattern to extract the rhythm features of EEG data, we evaluate the performance of the proposed method in the datasets from previous BCI Competitions. By making comparison with those of LDA, SVM and original BPR, our proposed method shows the better classification accuracy.

Keywords

biomimetic pattern recognition hyper sausage neuron sparse representation brain-computer interface motor imagery 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yanbin Ge
    • 1
  • Yan Wu
    • 1
  1. 1.Department of Computer Science and TechnologyTongji UniversityShanghaiChina

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