[Eeglablist] Measure projection
rcoopea at essex.ac.uk
Tue Aug 12 03:39:52 PDT 2014
Thanks for your reply. More participants were included when using MP than clustering (plus there are beautiful plots!). I'm using MP to choose which ICs and their activations to analyse from each participant. Do you think it is best to exclude participants who didn't contribute any ICs to the MP analysis? I could try to find the most similar looking IC from each excluded participant but this seems inaccurate and would probably add lots of error.
From: Makoto Miyakoshi [mmiyakoshi at ucsd.edu]
Sent: 11 August 2014 19:53
To: Cooper, Rachel
Cc: eeglablist at sccn.ucsd.edu; Nima Bigdely Shamlo
Subject: Re: [Eeglablist] Measure projection
> Now that I have run measure projection however, I've found that the problem persists.
If I understand it correctly, MP does not solve the problem completely, but it reduces it. If you smooth the data with 3-D Gaussian kernal (default with 8 mm, but try 20 mm also) you have *more chance* to overlap more subject's ICs in a given 'domain' than not.
I heard from Tim Mullen that he would release Bayesian Hierachical whatever to solve the 'missing data problem' in this approach. We will start the test phase soon.
On Wed, Jul 30, 2014 at 4:01 PM, Cooper, Rachel <rcoopea at essex.ac.uk<mailto:rcoopea at essex.ac.uk>> wrote:
Following advice given from members of this list (thank you) I used the measure projection add-on to try to find similar ICs across my participants/conditions. Using measure projection was recommended as a solution to the problem I had when clustering. The problem with clustering was that ICs from some participants didn't appear in a cluster and some participants contributed multiple ICs to a cluster. Now that I have run measure projection however, I've found that the problem persists. Could this be due to a mistake in running the MP analysis? What should I do with the participants who's ICs did not contribute to a domain?
Department of Psychology,
University of Essex,
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Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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