[Eeglablist] How many clusters to choose for a STUDY?

[Aleksandra Vuckovic]

Thanks Tarik,

Could you recommend me any published paper where (to your opinion) the methodology of choosing clusters is well described?
Regards,
Alex
________________________________
From: Tarik S Bel-Bahar [mailto:tarikbelbahar at gmail.com]
Sent: 06 March 2012 01:09
To: Aleksandra Vuckovic
Cc: eeglablist at sccn.ucsd.edu; Joaquin Rapela
Subject: Re: [Eeglablist] How many clusters to choose for a STUDY?

as in psychometrics and factor analyses,
it is a good idea to evaluate different clustering solutions, and checking the stability of one's main pattern of IC findings,
another option is to simply use corrmap to find all the similar brain ICs
 (for example all the ICs reflecting a traditional P1 component topography and timecourse).
Clusters that you develop should generally match up these similar scalp maps, even if you use
other information such as dipoles, ersp, etc.. as clustering information.
Another option is to attempt use of  the measure projection plugin as an alternative to clustering, but there are still
user decisions to be made there as well.
What we could use, generally speaking, is a measure of how much variance in the data is accounted for by each cluster solution,
compared to other solutions.
all the best! please let us know your choice of solution!


On Mon, Mar 5, 2012 at 3:29 PM, Joaquin Rapela <rapela at ucsd.edu<mailto:rapela at ucsd.edu>> wrote:
Dear Aleksandra,

I select the number of clusters in such a way that (ideally) each cluster has one component from every cluster. That is if my study contains 27 subjects, I select the number of subjects so that each cluster contains 27 components from 27 subjects. Of course, this is only an ideal scenario, but one that could guide you to a good number of clusters.

After you have decided on a number of clusters, and analyzed your data with these number of clusters, it is a good practice to repeat the analysis with a slightly different number of clusters, to get an idea of the robustness of your conclusions.

Cordially, Joaquin

On Mon, Mar 05, 2012 at 09:41:08PM +0000, Aleksandra Vuckovic wrote:
> Dear all,
> I'm clustering ICAs of three groups in a STUDY and was just wondering what would be the best indicator for how many clusters are just right (apart for experimentally testing different numbers). I've noticed that some cluster contain more than 100 components while some other 20-30 IC . Does it mean that this with 100 component is too large so I should go for larger number of clusters to separate this cluster in two, or 20-30 CI is too small number and I should reduce the total number of clusters?
> Many thanks,
> Aleksandra
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Joaquin Rapela, PhD
Swartz Center for Computational Neuroscience
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