用FastICA和Fisher准则提取脑电信号特征

EEG Feature Extraction Based on FastICA and Fisher Discriminant Criterion

脑-机接口(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拼写范式是一种常用的脑-机通信方法。它介绍了一种基于P300视觉诱发电位的脑电信号特征提取方法,选取三名实验者的数据用于实验分析。采用独立分量分析的固定点算法(FastICA)和Fisher准则进行特征提取,用支持向量机对提取的特征数据分类,并与主分量分析和Fisher准则相结合的特征提取方法作了比较,FastICA有很好的特征提取能力。

Brain-computer interaction （BCI） establishs a direct communication and control channel between human and computer or other electronic device by electroencephalogram （EEG）. P300-based speller paradigm is an common communication between Brain and computer. A feature extraction method based on P300 visual evoked potential is introdued. Three subjects is used to analyse. Fixed point of independent component analysis （FastICA） and Fisher discriminant criterion are imploied to implement the feature extraction, and uses support vector machines （SVM） to classify EEG signal. Compared with the feature extraction based on PCA and Fisher discriminant criterion, it has a good ability to extract feature.