Recipes for the linear analysis of EEG
Recipes for the linear analysis of EEG
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DOI：
10.1016/j.neuroimage.2005.05.032
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发表时间：
2005-11-01
期刊：
影响因子：
5.7
通讯作者：
Sajda, P
中科院分区：
文献类型：
--
作者：
Parra, LC;Spence, CD;Sajda, P
在本文中，我们描述了一组用于分析高空间密度脑电图的简单“方法”。我们专注于多个通道的线性集成，用于提取各个成分，而不进行任何空间或解剖建模假设，而是需要特定的统计属性，例如最大差异、最大功效或统计独立性。我们演示了如何使用相应的算法（例如线性判别分析、主成分分析和独立成分分析）来消除眼动伪影、提取强诱发反应以及分解时间重叠成分。一般方法被证明与脑电图的基础物理一致，它指定了基础神经和非神经电流源的线性混合模型。 (c) 2005 Elsevier Inc. 保留所有权利。
In this paper, we describe a simple set of "recipes" for the analysis of high spatial density EEG. We focus on a linear integration of multiple channels for extracting individual components without making any spatial or anatomical modeling assumptions, instead requiring particular statistical properties such as maximum difference, maximum power, or statistical independence. We demonstrate how corresponding algorithms, for example, linear discriminant analysis, principal component analysis and independent component analysis, can be used to remove eye-motion artifacts, extract strong evoked responses, and decompose temporally overlapping components. The general approach is shown to be consistent with the underlying physics of EEG, which specifies a linear mixing model of the underlying neural and non-neural current sources. (c) 2005 Elsevier Inc. All rights reserved.