[Eeglablist] What to do with more than one IC per subject in a cluster

[Makoto Miyakoshi]

Dear Arno,

I also want to know how to merge two ICs.

Makoto

2013/3/10 Aleksandra Vuckovic <Aleksandra.Vuckovic at glasgow.ac.uk>

> Dear Arno,
> could you please give us an example of code how to merge 2 components of
> one subject in a cluster (which I assume then behaves as one new component?)
> Many thanks,
> Aleksandra
>
> ________________________________________
> From: eeglablist-bounces at sccn.ucsd.edu [eeglablist-bounces at sccn.ucsd.edu]
> On Behalf Of Arnaud Delorme [arno at ucsd.edu]
> Sent: 09 March 2013 10:12
> To: James Schaeffer
> Cc: eeglablist at sccn.ucsd.edu
> Subject: Re: [Eeglablist] What to do with more than one IC per subject in
> a     cluster
>
> A word of caution here "Is it inappropriate to include more than one
> component from the same subject in the analysis".
>
> Yes and no.
> No because your Null hypothesis is modified. When you include more than
> one component from the same subject, you are not making inference about the
> general population of subjects any more but instead about components of the
> specific subjects you are studying. It is all a matter of how many
> components you have per subject compared to the number of subject. For
> example, if you have on average 1 component per subject (some subjects
> having 0, some other 2 component in the cluster), and you have 200
> subjects, then the original null hypothesis (which allows to make inference
> about the general population of subject) is mostly preserved. If you have
> 10 subjects and 10 components per subject, it is not.
>
> In general, I prefer either (1) to use 1 component per subject per cluster
> because this avoids having to compromise with the statistics or (2) merge
> components from the same subject in the cluster before performing any
> statistics. In (2), if you have for example 2 components for one subject,
> the ERP of both components will be pooled (pondered by the ICA inverse
> weight matrix). Then each subject will have either 1 or 0 ERP for each
> condition (if you have different conditions) and you may perform group
> statistics on these ERPs. This way you also have at most 1 component per
> subject - although when you pool components, it is not really an ICA
> component anymore. Unfortunately, there is no automated way to perform (2)
> in EEGLAB at the moment.
>
> Using several components per subjects is fine as well as long as you are
> aware that you are slightly compromising your null hypothesis (see above).
>
> Best,
>
> Arno
>
> On 27 Feb 2013, at 17:17, James Schaeffer wrote:
>
> > Dear Eeglablist,
> >
> > After clustering components using k-means, some of my clusters contain
> more than one component from a single subject.  I want to compare ERSPs
> using permutation analysis.  Is it inappropriate to include more than one
> component from the same subject in the analysis? If so, what is a good
> method for selecting which components to use?
> >
> > Thanks for your help,
> > James
> >
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-- 
Makoto Miyakoshi
JSPS Postdoctral Fellow for Research Abroad
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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