[Eeglablist] is there pca in eeglab

Fri Apr 19 21:34:47 PDT 2019

Hi Joseph,

I have had a look at your paper, and, correct me if I am wrong, it seems that all of your simulations assume that the true ERP is hidden in background EEG noise. Scott’s 2002 paper (Makeig S, Westerfield M, Jung T-P, Enghoff S, Townsend J, Courchesne E, Sejnowski TJ. Dynamic brain sources of visual evoked responses. Science, 295:690-694) showed that ERPs often arise from phase synchronization in different trials - and true ERPs are rarely observed. For some of the ERP, the inter-trial reliability (as measure using inter-trial coherence) can be as low as 0.1 (N400) and even P300 rarely goes above 0.5 (1 would be a true ERP as in your simulations). I would therefore argue that your simulations might not reflect true EEG/ERP activity.

I think a test that would convince me of the superiority of PCA over ICA in the time domain would be to extract P300 components (in the temporal domain), and assess how many trials are needed to reach significance when using each method as in Kappenman and Luck article https://www.ncbi.nlm.nih.gov/pubmed/20374541.

Cheers,

Arno

> On Apr 19, 2019, at 3:37 PM, Joseph Dien <jdien07 at mac.com> wrote:
> 
> Hi Arno,
>    with the greatest respect for all the wonderful work you’ve done for the EEG community with EEGlab, I have to disagree with this statement.  As I’ve shown empirically in my papers, ICA is indeed very good in the spatial domain (with electrodes as the variables) and is my preferred method for eyeblink correction, but it is not as good as PCA-Promax at ERPs in the temporal domain.  This follows naturally from the nature of the rotational criteria and the characteristics of the data.  Which is needed for an analysis depends on the analysis goals.  A couple such citations follow.
> 
> Dien, J., Khoe, W., & Mangun, G. R. (2007). Evaluation of PCA and ICA of simulated ERPs: Promax versus Infomax rotations. Human Brain Mapping, 28(8), 742-763. 
> 
> Dien, J. (2010). Evaluating Two-Step PCA Of ERP Data With Geomin, Infomax, Oblimin, Promax, And Varimax Rotations. Psychophysiology, 47(1), 170-183. 
> 
> Also, thanks Tarik for bringing Olav’s website to my attention.  This is indeed my Toolkit that he is posting to github.  I have no idea why he is doing this.  I’ll have to have a word with him.  I would appreciate folks downloading the EP Toolkit directly from my own sourceforge site as I use the download figures to seek grant funding, just as the eeglab team does.
> 
> Joe
> 
>> On Nov 23, 2018, at 19:25, Arnaud Delorme <arno at ucsd.edu> wrote:
>> 
>> There is also a PCA plugin in one of the workshop lectures (page 15).
>> 
>> https://sccn.ucsd.edu/mediawiki/images/9/95/EEGLAB2018_scripting5.pdf
>> 
>> We do not recommend using PCA which does not capture the structure of the data as ICA does. The paper Tarik mentioned is a good one
>> 
>> Artoni, F., Delorme, A., & Makeig, S. (2018). Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition. NeuroImage, 175, 176-187.
>> 
>> See also
>> 
>> Delorme A, Palmer J, Onton J, Oostenveld R, Makeig S. (2012) Independent EEG sources are dipolar.PLoS One, 7(2).
>> https://www.ncbi.nlm.nih.gov/pubmed/22355308
>> 
>> Best,
>> 
>> Arno
>> 
>> 
>> 
>>> On Nov 23, 2018, at 12:17 PM, Tarik S Bel-Bahar <tarikbelbahar at gmail.com> wrote:
>>> 
>>> Hello A S,
>>> Some brief notes below. When you reach a solution that satisfies your needs, please share it with the list so that other users can benefit from it.
>>> 
>>> *There are functions named runpca and runpca2 in the eeglab distribution. Review their documentation and test them out, as they may not be regularly used.
>>> *There is also Joe Dien's PCA toolkit: https://sourceforge.net/projects/erppcatoolkit/
>>> *There is also the following, which I believe uses Dien's tools or is a replica of them https://github.com/krigolson/MATLAB-EEG-PCA-Toolbox 
>>> *There are also some PCA functions in the Fieldtrip LIte folder that is part of the eeglab distribution (search for m files with pca in their title)
>>> 
>>> Also, ICA in eeglab is not just for removing artifacts. Many researchers analyze the ICs themselves as indexes of unique neural sources.
>>> From my understanding, eeglab developers strongly recommend ICA and NOT PCA, you can google "eeglablist + ICA + PCA" for past posts about that.
>>> There is a PCA flag in the runica function in eeglab, but it will essentially run ICA on PCA-reduced data. 
>>> The following recent article is interest,findable on google scholar: Artoni, F., Delorme, A., & Makeig, S. (2018). Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition. NeuroImage, 175, 176-187.
>>> 
>>> 
>>> 
>>> 
>>> 
>>> On Fri, Nov 23, 2018 at 12:00 PM A S <eng.emetsasa at gmail.com> wrote:
>>> Hi all,
>>> I know there's ICA in EEGLAB to remove artifacts. However I want to
>>> use PCA (Principal Components Analysis) to reduce the electrodes to
>>> spatio-temporal information according to the regions of interest. I
>>> can't find the PCA. Is there PCA in EEGLAB?
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>> 
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> 
> --------------------------------------------------------------------------------
> 
> Joseph Dien, PhD
> Senior Research Scientist
> Department of Human Development and Quantitative Methodology
> University of Maryland, College Park
> E-mail: jdien07 at mac.com
> Cell Phone: 202-297-8117
> http://joedien.com
> 