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Application of SVM Framework for Classification of Single Trial EEG

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

A brain-computer interface (BCI) system requires effective online processing of electroencephalogram (EEG) signals for real-time classification of continuous brain activity. In this paper, based on support vector machines (SVM), we present a framework for single trial online classification of imaginary left and right hand movements. For classification of motor imagery, the time-frequency information is extracted from two frequency bands (μ and β rhythms) of EEG data with Morlet wavelets, and the SVM framework is used for accumulation of the discrimination evidence over time to infer user’s unknown motor intention. This algorithm improved the single trial online classification accuracy as well as stability, and achieved a low classification error rate of 10%.

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Authors and Affiliations

  1. Center of NeuroInformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, P.R. China

    Xiang Liao, Yu Yin, Chaoyi Li & Dezhong Yao

Authors
  1. Xiang Liao
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  2. Yu Yin
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  3. Chaoyi Li
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  4. Dezhong Yao
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Liao, X., Yin, Y., Li, C., Yao, D. (2006). Application of SVM Framework for Classification of Single Trial EEG. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_80

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  • DOI: https://doi.org/10.1007/11760191_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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