According to the signal processing of EEG in brain-computer interface ( BCI) , a generalized kernel linear discriminant analysis ( GKLDA ) method based on kernel method and generalized singular value decomposition ( GSVD) was proposed to extract feature of EEG with two classes. First, the samples were mapped using the linear discriminant analysis in the feature space defined by a nonlinear mapping through kernel functions. Secondly, a nonlinear spatial filtering was solved through the GSVD which can solve the small sample size problem. In the experiment, the GKLDA was contrasted with kernel common spatial pattern (KCSP) , kernel linear discriminant analysis (KDA) and generalized linear discriminant analysis ( GDA ) for three public data which are dataset of BCI Competition Ⅰ , dataset Ⅳ of BCI Competition Ⅱ and S4b in dataset Ⅲ IB of BCI Competition Ⅲ. The same method was used on the dataset from ourselves with the fisher linear discriminant analysis classifier. The accuracy of the propose GKLDA feature of the three data are 93% ,77% , 80% , and 97% on the dataset from ourselves, better than the other kernel method. Experiment results indicate that, the GKLDA method can be well a new effective feature extraction method in brain computer interface.%针对脑机接口中脑电信号处理,提出了一种基于核方法和广义奇异值分解(GSVD)的广义核线性判别分析( GKLDA)方法,对两类脑电信号进行特征提取.首先在非线性核函数映射的核空间对样本做线性判别分析,针对“小样本采样问题”,采用GSVD求解一种非线性空域滤波器.算法验证中,采用BC1竞赛一数据集、竞赛二数据集Ⅳ和竞赛三数据集ⅢB中S4b等3组公开数据,以及一组自行采集的想象左右手运动的数据,同时分别与核共空间模式(KCSP)、核线性判别分析(KDA)、广义判别分析(GDA)进行对比.分类器采用Fisher线性判别分析分类器.所提出的方法针对3组公开数据,正确率分别为93％、77％、80％,自行数据正确率为97％,且优于其他几种核方法.实验结果表明,GKLDA方法是脑机接口中一种新的有效的特征提取方法.
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