Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine
AbstractIn 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. View Full-Text
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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.
Li X, Chen X, Yan Y, Wei W, Wang ZJ. Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine. Sensors. 2014; 14(7):12784-12802.Chicago/Turabian Style
Li, Xiaoou; Chen, Xun; Yan, Yuning; Wei, Wenshi; Wang, Z. J. 2014. "Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine." Sensors 14, no. 7: 12784-12802.