[Eeglablist] ICA after PCA

[Makoto Miyakoshi]

Dear Iman,

> I have found few papers and discussions about doing PCA and then ICA for
increasing the K-factor and dimensionality reduction.

K-factor you mean is the term you see in the equation?

minimumDataPointsToRunICA = ((number of channels)^2) * K

> However, I cannot completely understand what is the meaning of the ICA
outputs?

ICA output rotates PCA-dimension-reduced data. You can reconstruct
full-channel (but now rank-deficient) data using PCA output.

Actually there is another rotation, sphering, as a preprocess for ICA. So
you rotate data three times to find unmixing matrix using PCA
rank-reduction option. That's why you feel dizzy :-)

Makoto



On Wed, Apr 13, 2016 at 11:59 PM, Iman Mohammad-Rezazadeh <
irezazadeh at ucdavis.edu> wrote:

> 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/
>
>
>
>
>
>
>
>
>



-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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