[Eeglablist] PSD: ICA+PCA?

Thu Dec 29 16:14:38 PST 2016

Dear Alberto,

> How do I select component 1?

Use EEG.icaact(1,:,:) instead.

> Since I just reduced all the ICA components to 1 doing PCA, shouldnt I
select component 1 (out of 1)?

Yes.

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

Right. If you use IC(s), then these ICs contributes to multiple channels so
you can claim that you are using full part of data (channels).

Makoto

On Mon, Dec 19, 2016 at 10:49 AM, Alberto Sainz <albertosainzc at gmail.com>
wrote:

> 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!
>>>>>
>>>>> _______________________________________________
>>>>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>>>>> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.uc
>>>>> sd.edu
>>>>> For digest mode, send an email with the subject "set digest mime" to
>>>>> eeglablist-request at sccn.ucsd.edu
>>>>>
>>>>
>>>>
>>>>
>>>> --
>>>> 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
>>
>
>

-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20161229/8f59b6d3/attachment.html>