Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

Yuliang Ma, Xiaohui Ding, Qingshan She, Zhizeng Luo, Thomas Potter, Yingchun Zhang

Research output: Contribution to journalArticle

51 Scopus citations

Abstract

Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals.

Original languageEnglish (US)
Article number4941235
JournalComputational and Mathematical Methods in Medicine
Volume2016
DOIs
StatePublished - 2016

ASJC Scopus subject areas

  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Applied Mathematics

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