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. 2016:2016:4941235.
doi: 10.1155/2016/4941235. Epub 2016 May 30.

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

Yuliang Ma  1 , Xiaohui Ding  1 , Qingshan She  1 , Zhizeng Luo  1 , Thomas Potter  2 , Yingchun Zhang  3
Affiliations

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

Yuliang Ma et al. Comput Math Methods Med. 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.

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Figures

Figure 1
Figure 1
The timing chart of a single motor imagery experiment.
Figure 2
Figure 2
The timing chart of a single motor imagery experiment.
Figure 3
Figure 3
The flowchart of PSO optimized SVM parameters.
Figure 4
Figure 4
The fitness curve of the particle swarm optimization parameters.
Figure 5
Figure 5
The classification accuracy figure before optimization.
Figure 6
Figure 6
The classification accuracy figure after optimization.
Figure 7
Figure 7
The classification accuracy of PSO-SVM compared with traditional methods for 2005 Data Iva.
Figure 8
Figure 8
The classification accuracy of PSO-SVM compared with traditional methods for 2008 Dataset 1.
See this image and copyright information in PMC

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