Measure projection analysis: a probabilistic approach to EEG source comparison and multi-subject inference.
Measure projection analysis: a probabilistic approach to EEG source comparison and multi-subject inference.
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DOI：
10.1016/j.neuroimage.2013.01.040
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发表时间：
2013-05-15
期刊：
影响因子：
5.7
通讯作者：
Makeig, Scott
中科院分区：
文献类型：
--
作者：
Bigdely-Shamlo, Nima;Mullen, Tim;Kreutz-Delgado, Kenneth;Makeig, Scott
A crucial question for the analysis of multi-subject and/or multi-session electroencephalographic (EEG) data is how to combine information across multiple recordings from different subjects and/or sessions, each associated with its own set of source processes and scalp projections. Here we introduce a novel statistical method for characterizing the spatial consistency of EEG dynamics across a set of data records. Measure Projection Analysis (MPA) first finds voxels in a common template brain space at which a given dynamic measure is consistent across nearby source locations, then computes local-mean EEG measure values for this voxel subspace using a statistical model of source localization error and between-subject anatomical variation. Finally, clustering the mean measure voxel values in this locally consistent brain subspace finds brain spatial domains exhibiting distinguishable measure features and provides 3-D maps plus statistical significance estimates for each EEG measure of interest. Applied to sufficient high-quality data, the scalp projections of many maximally independent component (IC) processes contributing to recorded high-density EEG data closely match the projection of a single equivalent dipole located in or near brain cortex. We demonstrate the application of MPA to a multi-subject EEG study decomposed using independent component analysis (ICA), compare the results to k-means IC clustering in EEGLAB (sccn.ucsd.edu/eeglab), and use surrogate data to test MPA robustness. A Measure Projection Toolbox (MPT) plug-in for EEGLAB is available for download (sccn.ucsd.edu/wiki/MPT). Together, MPA and ICA allow use of EEG as a 3-D cortical imaging modality with near-cm scale spatial resolution.
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影响因子：
5.7
作者：
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通讯作者：
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DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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