[Eeglablist] Paper on ICA group source measure analysis published

[Scott Makeig]

All -

Nima Bigdely-Shamlo, Tim Mullen, Ken Kreutz-Delgado and I have published a
paper on a new approach to independent component clustering and measure
projection. Nima's Measure Projection Toolbox implementing the method for
EEGLAB is available at http://sccn.ucsd.edu/wiki/MPT

*Online accepted version*:
http://www.sciencedirect.com/science/article/pii/S1053811913000876

*Ttitle*:  Measure Projection Analysis: A Probabilistic Approach to EEG
Source Comparison and Multi-Subject Inference. *Neuroimage*, 2013

*Abstract*: 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.

-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation, University of
California San Diego, La Jolla CA 92093-0559, http://sccn.ucsd.edu/~scott
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