Independent EEG sources are dipolar.
Independent EEG sources are dipolar.
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DOI：
10.1371/journal.pone.0030135
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Makeig S
中科院分区：
文献类型：
--
作者：
Delorme A;Palmer J;Onton J;Oostenveld R;Makeig S
独立分量分析(ICA)和盲源分离(BSS)方法被越来越多地用于分离脑电(EEG)和其他电生理记录中体积传导混合的单个脑和非脑源信号。我们比较了22个ICA和BSS算法对13个71通道的人头皮脑电信号的分解结果，评估了头皮通道对中的成对互信息(PMI)、分量对中的剩余PMI、每个分解所影响的总体互信息缩减(MIR)，以及分解的偶极定义为与单一等效偶极子的投影匹配且小于给定残差方差的分量图的数量。性能最差的算法是主成分分析(PCA)；性能最好的是AMICA和其他基于似然/互信息的ICA方法。虽然这些和其他常用的分解方法返回了许多相似的成分，但18种ICA/BSS算法意味着偶极性随MIR和PMI的线性变化而变化，这一结果与许多最独立的EEG成分的解释一致，即许多最大独立的脑电成分是单个致密皮层区域内部分同步的局部皮质场活动的体积传导投影。为了鼓励进一步的方法比较，已经提供了用于准备结果的数据和软件(http://sccn.ucsd.edu/wiki/BSSComparison).
Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).
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