[Eeglablist] Removing redundancies from overlapping epochs prior to ICA

[Arnaud Delorme]

Dear Michael,

This is usually not necessary. Some data will be overrepresented but this does not break ICA. If you are concerned about that, extract shorter data epochs, apply ICA and then use the ICA decompotion in the original dataset (it is possible to do that in menu item Edit > Dataset Info).

Best,

Arno

> On Jun 19, 2015, at 11:55 AM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu> wrote:
> 
> Dear Michael,
> 
> Let's say you have stimulus presentation every 1 second.
> When you epoch data, epoch it to -1 to 2 sec (Dataset A) and 0 to 1 sec (Dataset B). You thoroughly clean Dataset B for ICA. When done, copy ica weight matrix from Dataset B to Dataset A (you can do it using GUI) and run dipfit. If necessary, clean Dataset A mildly.
> 
> > putting the epochs back after ICA?
> 
> This is technically feasible but complicated. I don't recommend it unless you have special reason to do it.
> 
> If you are interested developing your own tool outside EEGLAB, I'd like to point you to one of my plugins ARfitStudio in which I use mcolon() to crop out and put back epochs efficiently.
> 
> Makoto
> 
> On Wed, Jun 17, 2015 at 4:06 PM, Michael Spezio <mspezio at scrippscollege.edu <mailto:mspezio at scrippscollege.edu>> wrote:
> Hi Makoto,
> 
> 
> Do you have an efficient way of removing data redundancies that are due to overlapping epochs prior to ICA, and then putting the epochs back after ICA? I noticed that in your pipeline, you say that overlapping epochs are fine but they must be handled to avoid redundancy prior to ICA.
> 
> 
> How best to do this so that one can add back the required epochs following ICA?
> 
> 
> Thanks for your help with this.
> 
> Best,
> 
> 
> Michael
> 
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> 
> 
> -- 
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
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