Study of removal of artifacts in MEG using PCA and ICA

被引：0

作者：

Gao, Li ^{[1
]}

Huang, Li-Yu ^{[1
]}

Ding, Cui-Ling ^{[2
]}

机构：

[1] School of Electronic Engineering, Xidian Univ., Xi'an 710071, China

[2] Xijing Hospital, Fourth Military Medical Univ., Xi'an 710032, China

来源：

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2007年 / 34卷 / 06期

关键词：

Algorithms - Blind source separation - Independent component analysis - Principal component analysis - Signal processing - Signal reconstruction - Signal to noise ratio;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are two representative algorithms in Blind Source Separation. In this paper, a novel method for removal of artifacts in Magnetoencephalography (MEG) by combining PCA and ICA is presented. The basic concepts and algorithms of PCA and ICA are introduced firstly, MEG data are decomposed by PCA method in order to reduce the dimension of the original signals and take the redundancies out for getting the main components of data. Then the de-dimensioned data are further processed by using the adaptive Infomax algorithm of ICA. The study shows that the various artifacts can be separated from the MEG successfully and that removal of artifacts can be realized by signal reconstruction.

引用

页码：939 / 943

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