[Eeglablist] ICA after PCA

[Makoto Miyakoshi]

Dear Iman,

It does not increase K directly, but effectively it does.
If you have 100ch and K==30 (fixed), you need (100^2) * 30 data points.
But if you apply PCA on it and reduce it to 10 PCs, you need only (10^2) *
30 data points. In this case, your K is effectively multiplied by
100^2/10^2 ==100.

Note that the value K should increase exponentially as the number of
channels increases. K==30 is for the case of 32ch. There is no good study
on the question how many data points are necessary for stable ICA results,
but Jason says 1 million datapoints for 100ch is a rule of thumb (in this
case, K==100).

Makoto

On Thu, Apr 21, 2016 at 12:22 AM, Iman Mohammad-Rezazadeh <
irezazadeh at ucdavis.edu> wrote:

> Hi Makoto,
>
> My question is : does doing PCA before ICA helps for increasing K or in
> other words the quality of ICA output? Could you please describe it in a
> simple language J
>
> Thanks
>
> Iman
>
> *From:* Makoto Miyakoshi [mailto:mmiyakoshi at ucsd.edu]
> *Sent:* Wednesday, April 20, 2016 7:01 PM
> *To:* Iman Mohammad-Rezazadeh <irezazadeh at UCDAVIS.EDU>
> *Cc:* EEGLAB List <eeglablist at sccn.ucsd.edu>; Arnaud Delorme <
> arno at ucsd.edu>; Loo, Sandra <SLoo at mednet.ucla.edu>; Jeste, Shafali M.D. <
> SJeste at mednet.ucla.edu>; ADickinson at mednet.ucla.edu; Scott Makeig <
> smakeig at ucsd.edu>
> *Subject:* Re: ICA after PCA
>
>
>
> 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
>



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