事件诱发电位信号分类的时空特征提取方法

准确对事件诱发电位(ERPs)进行分类,对于各种人类认知研究和临床医学评估非常有意义.由于ERPs信号是非常高维的数据,而且其中包含非常多的与分类无关的信息,从ERPs信号中提取特征尤显重要.分析了共空间模式(CSP)的原理和不足,引入自回归(AR)模型与白化变换相结合,提出了针对ERPs分类的时空特征提取方法,并设计了验证该方法的认知实验,在认知实验数据上分别用时空特征提取方法与CSP提取特征,用同样的分类器支持向量机(SVM)训练分类器,比较它们的分类效果.实验表明,在ERPs分类问题上,时空特征提取方法与CSP相比具有明显的优势,在参数确定合理的情况下,时空特征提取方法可使分类准确率达到90%以上.

Extracting spatio-temporal feature for classification of event-related potentials

The accurate classification of ERPs is very important for numerous human cognition studies and clinical evaluations. Extracting feature from ERPs is very important due to high dimension of ERPs which includes much information having nothing to do with classification. The principle and weakness of CSP were analyzed and the method to extract spatio-temporal feature by combining AR model and Whiten transformation was proposed. Cognitive experiments were designed to verify our method. Two kind of features were extracted from the data collected from the cognitive experiments separately by spatio-temporal method and CSP, the classifiers were trained both by SVM, and compared the two on effectiveness of classification were compared. The result demonstrates the spatio-temporal feature method is clearly superior to CSP in the classification of ERPs and the precision rate of classification based on spatio-temporal feature method may be over 90% if the parameters are reasonably determined.