CLASSIFICATIONS OF EEG DURING MENTAL TASK BASED ON SUPPORT VECTOR MACHINE WITH OPTIMAL KERNEL-PARAMETER
基于优化核参数支持向量机的意识任务分类

The fundamental of support vector machine (SVM) based on structure risk minimization was introduced. An estimation formula of upper bound of generalization error was given, and the optimal kernel-parameter of the SVM was selected automatically by the formula. The feature vectors were extract-ed from six-channel electroencephalograph (EEG) data segments of four subjects under three mental tasks by the mean of a multivariate autoregressive (MVAR) model method. These vectors were considered as the inputs of classifiers to test classification accuracies for three task pairs. Average classification accura-cies indicated that the optimal kernel-parameter method could get optimal results, and was significantly better than that of Radial Basis Function (RBF) network.