基于主成分分析和Fisher准则的脑电信号分类

脑-机接口(brain-computer interaction，BCI)利用脑电信号实现人脑与计算机或其它电子设备的通讯和控制，P300拼写范式是脑机接口中的一种常用方法。将遗传算法和支持向量机用于脑电信号的分类。选取三个实验者的实验数据作为处理对象，采用主成分分析和Fisher准则相结合提取特征。在用主成分分析降维后，Fisher准则进一步提取有效特征，提升分类准确率。本文采用用支持向量机对特征数据分类。Fisher准则在特征提取中良好的效果。

classification of EEG Based on PCA and Fisher discriminant criteria

BCI establishs a direct communication and control channel between human and computer or other electronic device. P300-based speller paradigm is an common method for BCI. In this paper, genetic algorithm and support vector machines(SVM) are used for classification of EEG. It employs Principal Component Analysis(PCA) and Fisher Discriminant Criterion to implement the feature extraction. After using PCA to reduce dimension, Fisher discriminant Criterion can further extract effective features and improve the accuracy of classfication. This paper employs SVM to classify electroencephalogram.