title: Classification of EEG signals using a multiple kernel learning support vector machine.
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.
authors.affiliation: Shanghai Medical Instrumentation College, Shanghai 200093, China. xiaouli@gmail.com.
authors.forename: Xiaoou
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Biomedical Engineering, Hefei University of Technology, Hefei 230009, China. xunchen@ece.ubc.ca.
authors.forename: Xun
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Neurology, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China. quack008@126.com.
authors.forename: Yuning
authors.initials: Y
authors.lastname: Yan
authors.affiliation: Department of Neurology, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China. wenshiwei@medmail.com.cn.
authors.forename: Wenshi
authors.initials: W
authors.lastname: Wei
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada. zjanew@ece.ubc.ca.
authors.forename: Z Jane
authors.initials: ZJ
authors.lastname: Wang
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology; D062207:Brain-Computer Interfaces; D016022:Case-Control Studies; D003071:Cognition / Q000502:physiology; D004569:Electroencephalography / Q000295:instrumentation*; D005260:Female; D006801:Humans; D007858:Learning; D008297:Male; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D012984:Software; D060388:Support Vector Machine; D055815:Young Adult