TY - JOUR
T1 - Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization
AU - Ma, Yuliang
AU - Ding, Xiaohui
AU - She, Qingshan
AU - Luo, Zhizeng
AU - Potter, Thomas
AU - Zhang, Yingchun
N1 - Publisher Copyright:
© 2016 Yuliang Ma et al.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84975322675&partnerID=8YFLogxK
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U2 - 10.1155/2016/4941235
DO - 10.1155/2016/4941235
M3 - Article
C2 - 27313656
AN - SCOPUS:84975322675
SN - 1748-670X
VL - 2016
JO - Computational and Mathematical Methods in Medicine
JF - Computational and Mathematical Methods in Medicine
M1 - 4941235
ER -