Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization.
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.
Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
Yuliang
Y
Ma
Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
Xiaohui
X
Ding
Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
Qingshan
Q
She
Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
Zhizeng
Z
Luo
Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA.
Thomas
T
Potter
Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA; Guangdong Provincial Work Injury Rehabilitation Center, Guangzhou 510000, Chinas.
Yingchun
Y
Zhang
D000328:Adult; D000465:Algorithms; D003198:Computer Simulation; D004569:Electroencephalography; D005528:Foot / Q000502:physiology; D006225:Hand / Q000502:physiology; D064368:Healthy Volunteers; D006801:Humans; D007092:Imagination / Q000502:physiology*; D015233:Models, Statistical; D009048:Motor Skills; D009068:Movement; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D015398:Signal Transduction; D060388:Support Vector Machine