摘要
简要介绍了独立分量分析 (ICA)的基本思想及算法 ,并将其应用在基于多导思维脑电 (mentalEEG)的特征提取方面。实验结果表明 :ICA可以将脑电信号中包含的心电(ECG)、眼电 (EOG)等多种干扰信号成功地分离出来 ,较好地完成了脑电消噪预处理工作。同时 ,通过使用ICA方法对不同心理作业的脑电信号进行分析处理 ,发现了与心理作业相对应的脑电独立分量特征 ,这些稳定的独立分量特征为心理作业分类和脑—机接口技术提供了新的实现方法。
ICA is applied to the mental EEG signal analysis. In one side, our experiment results show that ICA can effectively detect, separate and remove a wide variety of artefacts from EEG recordings. For another, the ICA algorithm is used to the pattern extraction of mental EEG signals from different mental tasks. By studying the EEG independent sources and their projection on human scalp, we can find that some steady independent components always appear when the subject repeats the same mental tasks. The results will provide us a promising method in the classification of mental tasks and the research on Brain-Computer Interface(BCI) technology.
出处
《微机发展》
2002年第6期36-39,共4页
Microcomputer Development
基金
安徽省自然科学基金资助项目 (0 0 43 2 14 )
关键词
独立分量分析
思维脑电
脑电消噪
特征提取
ICA
心理作业
independent component analysis
mental EEG
artifacts cancellation
pattern extraction
mental task
