Application of SVM framework for classification of single trial EEG

  • Authors:

  • Xiang Liao;Yu Yin;Chaoyi Li;Dezhong Yao

  • Affiliations:

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

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2006

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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%.