title: New KF-PP-SVM classification method for EEG in brain-computer interfaces.
abstract: Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.
authors.affiliation: Department of Automation, School of Mechatronic Engineering and Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai 200072, China.
authors.forename: Banghua
authors.initials: B
authors.lastname: Yang
authors.affiliation: Department of Automation, School of Mechatronic Engineering and Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai 200072, China.
authors.forename: Zhijun
authors.initials: Z
authors.lastname: Han
authors.affiliation: Department of Automation, School of Mechatronic Engineering and Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai 200072, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Zan
authors.affiliation: Department of Automation, School of Mechatronic Engineering and Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai 200072, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology; D015233:Models, Statistical; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Kernel fisher; brain computer interface; common spatial pattern; posterior probability; support vector machine