Understanding brain connectivity from EEG data by identifying systems composed of interacting sources
Understanding brain connectivity from EEG data by identifying systems composed of interacting sources
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DOI：
10.1016/j.neuroimage.2008.04.250
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发表时间：
2008-08-01
期刊：
影响因子：
5.7
通讯作者：
Nolte, Guido
中科院分区：
文献类型：
--
作者：
Marzetti, Laura;Del Gratta, Cosimo;Nolte, Guido
在通过 EEG/MEG 理解和建模大脑功能时，不仅要能够识别活动区域，而且要了解不同区域之间的干扰。 EEG/MEG 信号是通过头部传导的基础脑源活动量的叠加产生的。体积传导的影响会在测量信号中产生寄生相互作用。为了理解干扰机制，将真实的源相互作用与噪声分开，并分解由相互作用的源组成的不同系统的贡献是至关重要的。作为先决条件，我们考虑分解不相关源对测量场的贡献的问题。这个问题相当于分解由相互作用的源组成的不同不相关复合系统的贡献的问题。为此，我们开发了一种基于主成分分析的方法，即源主成分分析（sPCA），它利用从线性逆方法估计的源正交性的基本假设，来提取信号空间中的基本特征。然后，我们考虑对包含使用 sPCA 识别的每个复合系统的相关源的贡献进行分层的问题。虽然 sPCA 正交性假设足以分离不相关的系统，但它无法分离每个系统内的各个组件。为了解决这个问题，我们引入了最小重叠分量分析（MOCA），采用纯空间标准来分解相关（或相干）源对。所提出的方法在模拟中进行了测试，并应用于来自人类 p 和 a 节律的 EEG 数据。 (C) 2008 Elsevier Inc. 保留所有权利。
In understanding and modeling brain functioning by EEG/MEG, it is not only important to be able to identify active areas but also to understand interference among different areas. The EEG/MEG signals result from the Superimposition Of underlying brain source activities volume conducted through the head. The effects of volume conduction produce spurious interactions in the measured signals. It is fundamental to separate true source interactions from noise and to unmix the contribution of different systems composed by interacting Sources in order to understand interference mechanisms.As a prerequisite, we consider the problem of unmixing the contribution Of uncorrelated sources to a measured field. This problem is equivalent to the problem Of unmixing the contribution of different uncorrelated compound systems composed by interacting sources. To this end, we develop a principal component analysis-based method, namely, the source principal component analysis (sPCA), which exploits the underlying assumption of orthogonality for sources, estimated from linear inverse methods, for the extraction of essential features in signal space.We then consider the problem of demixing the contribution of correlated sources that comprise each of the compound systems identified by using sPCA. While the sPCA orthogonality assumption is sufficient to separate uncorrelated systems, it cannot separate the individual components within each system. To address that problem, we introduce the Minimum Overlap Component Analysis (MOCA), employing a pure spatial criterion to unmix pairs of correlates (or coherent) sources. The proposed methods are tested in simulations and applied to EEG data from human p and a rhythms. (C) 2008 Elsevier Inc. All rights reserved.