[Eeglablist] ICA after PCA

Wed Apr 20 11:52:43 PDT 2016

Reposting !

Hi EEGLABers,
I have found few papers and discussions about doing PCA and then ICA for increasing the K-factor and dimensionality reduction. The (un)mixing matrix would be m x m which m is the number of PCA. Each row (column) is the weights for ICA sources.
However, I cannot completely understand what is the meaning of the ICA outputs? How are the IC maps (topo maps) constructed since we need the location of PCA components (similar to  the channels locations) to plot the spatial filters/IC maps.
In other words,  how can we plot the IC maps given the fact that we don't have the spatial information about PCA components?
Best
Iman

============================================
Iman M.Rezazadeh, Ph.D
UCLA David Geffen School of Medicine
Semel Institute for Neuroscience and Human Behavior
760 Westwood Plaza, Ste 47-448
Los Angeles, CA  90095
http://www.linkedin.com/pub/iman-m-rezazadeh/10/859/840/

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