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

Journal: Computational and mathematical methods in medicine
Published Date: May 30, 2016

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.

Authors

  • Yuliang Ma
    Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
  • Xiaohui Ding
    Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
  • Qingshan She
    Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
  • Zhizeng Luo
    Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
  • Thomas Potter
    Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA.
  • Yingchun Zhang
    Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA; Guangdong Provincial Work Injury Rehabilitation Center, Guangzhou 510000, China.

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