%0 Journal Article
%T CLASSIFICATIONS OF EEG DURING MENTAL TASK BASED ON SUPPORT VECTOR MACHINE WITH OPTIMAL KERNEL-PARAMETER<br>基于优化核参数支持向量机的意识任务分类
%A XUE Jian-zhong
%A YAN Xiang-guo
%A ZHENG Chong-xun
%A WANG Hao-jun
%A <br>薛建中
%A 闫相国
%A 郑崇勋
%A 王浩军
%J 生物物理学报
%D 2003
%I 
%X 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.
%K Structure
%K risk
%K Support
%K vector
%K machine
%K Mental
%K task
%K EEG<br>核参数
%K 支持向量机
%K 意识任务
%K 结构风险
%K 脑电
%K 神经网络
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=E0C9D9BBED813D6674AC13E942EAC86D&aid=4C11AC55ECDF032E&yid=D43C4A19B2EE3C0A&vid=2A8D03AD8076A2E3&iid=38B194292C032A66&sid=3224764AEAFCF8C2&eid=377D325742940769&journal_id=1000-6737&journal_name=生物物理学报&referenced_num=0&reference_num=7