[Eeglablist] PSD: ICA+PCA?

[Alberto Sainz]

Dear Makoto,

I meant to use PCA before ICA. In the paper described (link:
http://www.ijcaonline.org/archives/volume42/number15/5770-7993 ) they use
frist ICA and then PCA to reduce data to only one component.
My question is, how valid this could be? How much data do we lose doing
this?

Thanks for the code, I already took it before from the list :). My idea was
actually applying the code to the only component left after ICA+PCA.
Otherwise I would have to apply the code for each channel/component (that
was my previous idea).

In case doing PCA after ICA is not a good option, how would you average the
total frequency of all channels? By a simple avergaing code? Or maybe im
missing something and there is no point on averaging the power of all
channels/components!!

Sorry I know there are too many questions! I hope you understand what I
mean.

Thanks

2016-12-07 0:14 GMT+01:00 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>:

> Dear Alberto,
>
> > can anyone explain why we would perform first an ICA and then a PCA?
>
> You can apply PCA as a preprocessing for ICA. We sometimes do it (you
> know, runica() has an option 'pca' to reduce dimensions.)
>
> > Also, before reading this paper, my intention was to perform an ICA,
> average the power for each component in a specific frecuency band and then
> average those means for the same frequency.
>
> See my new help wiki page for how to do it.
>
> https://sccn.ucsd.edu/wiki/Makoto%27s_useful_EEGLAB_code
>
> > I guess this accumulates a lot of error, could anyone tell me if this
> procedure would be valid?
>
> Not necessarily. It's not the errors that accumulates, but you drop
> information.
> Dimension reduction by PCA or ICA (ICA results are also sorted by
> variance, so the near-last ICs are very small; removing them would not make
> visible differences but still reduces data ranks) means that you use less
> than 100% of data variance.
>
> Imagine you have 128 channel and only analyze Fz, Cz, and Pz. This is much
> more wasteful. Reasonable dimension reduction is indispensable for any
> high-dimensional data processing.
>
> > could anyone tell me if this procedure would be valid?
>
> It's a trade off between surveyability and amount of data--if you focus on
> less Independent/Principal components, you get more surveyability but
> loosing more information. If you use more ICs/PCs, data are hard to survey.
> You can't put all the info you have on a paper anyway, so selection is
> always necessary. You need courage to focus on data, I know!
>
> Makoto
>
>
> On Mon, Nov 28, 2016 at 6:57 AM, Alberto Sainz <albertosainzc at gmail.com>
> wrote:
>
>> Hello,
>>
>> I have a question regarding ICA and PCA.
>>
>> Following the paper "Power Spectrum Analysis of EEG Signals for Estimating
>> Visual Attention" to calculate Power Spectrum by frequencies, they
>> perform first an ICA and then a PCA.
>>
>> I understand that PCA concentrates the information in less components (in
>> this case in just one) so its easier to work with the data (in this case to
>> measure power by frequency bands). However, I think I miss something about
>> the ICA. My understanding is that ICA separates the signals to make them
>> independent. If this is the case, can anyone explain why we would perform
>> first an ICA and then a PCA? Which would be the sense of separating the
>> signals to concentrate them together again?
>>
>> Also, before reading this paper, my intention was to perform an ICA,
>> average the power for each component in a specific frecuency band and then
>> average those means for the same frequency. I guess this accumulates a lot
>> of error, could anyone tell me if this procedure would be valid?
>>
>> thanks!
>>
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>
>
>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
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