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Classification of Single-Trial EEG Based on Support Vector Clustering during Finger Movement

  • Boyu Wang
  • Feng Wan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)

Abstract

Classification of electroencephalogram (EEG) is an important and challenging issue for brain computer interface (BCI) system. In this paper, an algorithm based on common spatial subspace decomposition (CSSD) and support vector clustering (SVC) is proposed to classify single-trial EEG recording during left or right finger movement. The algorithm is tested by the dataset IV of “BCI competition 2003”, and the experimental result shows the proposed method, only using bereitschaftspotential (BP), rather than both BP and event-related desynchronization (ERD), has higher classification accuracy than the best one reported in the competition.

Keywords

Support vector clustering (SVC) Common spatial subspace decomposition (CSSD) Electroencephalogram (EEG) Brain computer interface (BCI) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Boyu Wang
    • 1
  • Feng Wan
    • 1
  1. 1.Department of Electrical and Electronics Engineering, Faculty of Science and TechnologyUniversity of MacauMacau

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