文献类型：期刊文献

英文题名：Automatic ocular artifact suppression from human operator's EEG based on a combination of independent component analysis and fuzzy c-means clustering techniques

作者：Wang, Raofen[1]; Zhang, Jianhua[1]; Wang, Xingyu[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200237, China

年份：2011

起止页码：3175

外文期刊名：Proceedings of the 30th Chinese Control Conference, CCC 2011

收录：EI(收录号：20113914369407)

语种：英文

外文关键词：Wavelet analysis - Entropy - Electroencephalography - Fuzzy systems

摘要：Independent component analysis (ICA) and fuzzy c-means (FCM) clustering were adopted for automatic ocular artifact suppression from operator's electroencephalogram. Firstly, ICA was applied to the 20s data containing nine channels of EEG data and one of electrooculagram (EOG) data. Secondly, each 20s independent component (IC) was partitioned into ten 2 s epochs. And five features of each epoch were calculated, which are wavelet entropy, power in the band between 0 and 5 Hz, kurtosis, mutual information and correlation. Thirdly, the epochs were classified as either EEG or ocular artifact based on the result of FCM clustering. And then components which were recognized as ocular artifact were rejected. Clean EEG was obtained. The result shows that the method based on ICA and FCM can be applied to online automatic ocular artifact suppression from EEG. ? 2011 Chinese Assoc of Automati.