[Eeglablist] PSD: ICA+PCA?

[Alberto Sainz]

Thank you very much Makoto!

I think i am going to use the PCA method in a small sample of recordings
and lets see what happens.

I was doing it right now and I got stucked in your code:

[spectra,freqs] = spectopo(EEG.data(2,:,:), 0, EEG.srate);

This selects channel 2 of the data. How do I select component 1? Since I
just reduced all the ICA components to 1 doing PCA, shouldnt I select
component 1 (out of 1)? I checked the spectopo help and I only see I can
select channels, not components.

If i select a channel then I am not taking all the data from the full EEG
recording, right?

Thank you!

2016-12-14 4:41 GMT+01:00 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>:

> Dear Alberto,
>
> PCA before ICA has a meaning of dimension reduction to allow ICA to have
> less model order (this is empirical though). Since you can project
> dimension-reduced PCs to channels any time, the meaning of the process is
> pretty straightforward.
>
> PCA after ICA is a bit tricky. I guess you can do that as long as your PCA
> (1st or up to 3rd or whatever) results show that good portion of the data
> variance is accounted for by those... in my personal experience, the first
> PC could explain 50-60% of time-frequency results across all ICs, which I
> thought is not too bad if I can say something interesting based upon it.
>
> > how would you average the total frequency of all channels?
>
> You can use ICA for the purpose of dimension reduction too; you count ICs
> until the 60% of the variance is accounted for, for example. Then what ICs
> does it select? That's a good question, isn't it?
>
> Relatedly, there is std_envtopo() plugin. It tells you which IC cluster
> explain how much of variance in grand averaged ERP envelope. This can tell
> you which cluster you should focus on, because it tells you who is most
> dominant in which ERP (envelope) component.
>
> Makoto
>
>
>
> On Mon, Dec 12, 2016 at 5:53 PM, Alberto Sainz <albertosainzc at gmail.com>
> wrote:
>
>> 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
>>>
>>
>>
>
>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
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