基于优化核参数支持向量机的意识任务分类  

CLASSIFICATIONS OF EEG DURING MENTAL TASK BASED ON SUPPORT VECTOR MACHINE WITH OPTIMAL KERNEL-PARAMETER

作　　者：薛建中[1] 闫相国[1] 郑崇勋[1] 王浩军[1] 

机构地区：[1]西安交通大学生物医学工程研究所,西安710049

出　　处：《生物物理学报》2003年第3期322-326,共5页Acta Biophysica Sinica

基　　金：国家自然科学基金项目(30170257)

摘　　要：根据支持向量机的基本原理,给出一种推广误差上界估计判据,并利用该判据进行最优核参数的自动选取。对三种不同意识任务的脑电信号进行多变量自回归模型参数估计,作为意识任务的特征向量,利用支持向量机进行训练和分类测试。分类结果表明,优化核参数的支持向量机分类器取得了最佳的分类效果,分类正确率明显高于径向基函数神经网络。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.

关 键 词：核参数 支持向量机 意识任务 结构风险 脑电 神经网络 

分 类 号：Q424[生物学—神经生物学] R318[生物学—生理学]