[Eeglablist] is there pca in eeglab

[Arnaud Delorme]

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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