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Sensors 2014, 14(7), 12784-12802; doi:10.3390/s140712784
Article

Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine

1,2
, 3,* , 4
, 4
 and 5
1 Shanghai Medical Instrumentation College, Shanghai 200093, China 2 School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 3 Department of Biomedical Engineering, Hefei University of Technology, Hefei 230009, China 4 Department of Neurology, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China 5 Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
* Author to whom correspondence should be addressed.
Received: 19 February 2014 / Revised: 20 June 2014 / Accepted: 10 July 2014 / Published: 17 July 2014
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Abstract

In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three stages: (1) a pre-processing step was performed to improve the general signal quality of the EEG; (2) the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3) a multiple kernel learning support vector machine (MKL-SVM) based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20%, 81.25%, 76.76%, and 75.25% respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24% and 80.33% for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI), especially for mental task classification and identifying suitable brain impairment candidates.
Keywords: brain computer interface; mental task; stroke patients; multiple kernel learning; polynomial kernel; radial basis function kernel brain computer interface; mental task; stroke patients; multiple kernel learning; polynomial kernel; radial basis function kernel
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Li, X.; Chen, X.; Yan, Y.; Wei, W.; Wang, Z.J. Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine. Sensors 2014, 14, 12784-12802.

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