Imaging brain dynamics using independent component analysis
Imaging brain dynamics using independent component analysis
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DOI：
10.1109/5.939827
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发表时间：
2001-07-01
影响因子：
20.6
通讯作者：
Sejnowski, TJ
中科院分区：
文献类型：
--
作者：
Jung, TP;Makeig, S;Sejnowski, TJ
脑电图（EEG）和脑磁图（MEG）记录的分析对于基础脑研究和医学诊断和治疗都是重要的。独立成分分析（伊卡）是一种有效的方法，以消除文物和分离的来源，从这些记录的大脑信号。一种类似的方法被证明对分析功能性磁共振脑成像（fMRI）数据很有用。在本文中，我们概述了伊卡的基本假设，并证明其应用程序的各种电气和血液动力学记录从人脑。
The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging (fMRI) data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain.