Removing electroencephalographic artifacts: Comparison between ICA and PCA

被引：125

作者：

Jung, TP ^{[1
]}

Humphries, C ^{[1
]}

Lee, TW ^{[1
]}

Makeig, S ^{[1
]}

McKeown, MJ ^{[1
]}

Iragui, V ^{[1
]}

Sejnowski, TJ ^{[1
]}

机构：

[1] Salk Inst, Computat Neurobiol Lab, San Diego, CA 92186 USA

来源：

NEURAL NETWORKS FOR SIGNAL PROCESSING VIII | 1998年

关键词：

D O I：

10.1109/NNSP.1998.710633

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Pervasive electroencephalographic (EEG) artifacts associated with blinks, eye-movements, muscle noise, cardiac signals, and line noise poses a major challenge for EEG interpretation and analysis. Here, we propose a generally applicable method for removing a wide variety of artifacts from EEG records based on an extended version of an Independent Component Analysis (ICA) algorithm [2, 12] for performing blind source separation on linear mixtures of independent source signals. Our results show that ICA can effectively separate and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably to those obtained using Principal Component Analysis.

引用

页码：63 / 72

页数：10