[Eeglablist] Measure projection

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Mon Aug 11 11:53:58 PDT 2014

Dear Rachel,

> 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> wrote:

>  Hi everyone,
> 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?
> Many thanks
> Rachel
>  Rachel Cooper
> PhD researcher
> Department of Psychology,
> University of Essex,
> Wivenhoe Park,
> Colchester,
> Essex,
> CO4 3SQ
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Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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