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

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Emerging Intelligent Computing Technology and Applications (ICIC 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 304))

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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.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Tongji University, 201804, Shanghai, China

    Yanbin Ge & Yan Wu

Authors
  1. Yanbin Ge
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  2. Yan Wu
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Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, School of Electronics and Information Engineering, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Phalguni Gupta

  3. Department of Chemistry, University of Louisville, 2320 South Brook Street, 40292, Louisville, Kentucky, USA

    Xiang Zhang

  4. School of Electrical, Computer & Telecommunications Engineering, The University of Wollongong,, 2522, North Wollongong, NSW, Australia

    Prashan Premaratne

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© 2012 Springer-Verlag Berlin Heidelberg

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Ge, Y., Wu, Y. (2012). A New Hybrid Method with Biomimetic Pattern Recognition and Sparse Representation for EEG Classification. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_31

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  • DOI: https://doi.org/10.1007/978-3-642-31837-5_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31836-8

  • Online ISBN: 978-3-642-31837-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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