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<p class=MsoNormal><font size=2 color=navy face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:navy'>Thanks Tarik,<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=navy face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:navy'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 color=navy face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:navy'>Could you recommend me any published paper
where (to your opinion) the methodology of choosing clusters is well described?<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=navy face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:navy'>Regards,<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=navy face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:navy'>Alex<o:p></o:p></span></font></p>
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<p class=MsoNormal><b><font size=2 face=Tahoma><span lang=EN-US
style='font-size:10.0pt;font-family:Tahoma;font-weight:bold'>From:</span></font></b><font
size=2 face=Tahoma><span lang=EN-US style='font-size:10.0pt;font-family:Tahoma'>
Tarik S Bel-Bahar [mailto:tarikbelbahar@gmail.com] <br>
<b><span style='font-weight:bold'>Sent:</span></b> 06 March 2012 01:09<br>
<b><span style='font-weight:bold'>To:</span></b> <st1:PersonName w:st="on">Aleksandra
Vuckovic</st1:PersonName><br>
<b><span style='font-weight:bold'>Cc:</span></b> <st1:PersonName w:st="on">eeglablist@sccn.ucsd.edu</st1:PersonName>;
Joaquin Rapela<br>
<b><span style='font-weight:bold'>Subject:</span></b> Re: [Eeglablist] How many
clusters to choose for a STUDY?</span></font><span lang=EN-US><o:p></o:p></span></p>
</div>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'><o:p> </o:p></span></font></p>
<div>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>as in psychometrics and factor analyses,<o:p></o:p></span></font></p>
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<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>it is a good idea to evaluate different clustering solutions, and
checking the stability of one's main pattern of IC findings,<o:p></o:p></span></font></p>
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<div>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>another option is to simply use corrmap to find all the similar brain
ICs<o:p></o:p></span></font></p>
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<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'> (for example all the ICs reflecting a traditional P1 component
topography and timecourse).<o:p></o:p></span></font></p>
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<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>Clusters that you develop should generally match up these similar scalp
maps, even if you use <o:p></o:p></span></font></p>
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<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>other information such as dipoles, ersp, etc.. as clustering
information.<o:p></o:p></span></font></p>
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<div>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>Another option is to attempt use of the measure projection plugin
as an alternative to clustering, but there are still<o:p></o:p></span></font></p>
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<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>user decisions to be made there as well.<o:p></o:p></span></font></p>
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<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>What we could use, generally speaking, is a measure of how much
variance in the data is accounted for by each cluster solution,<o:p></o:p></span></font></p>
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<div>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>compared to other solutions.<o:p></o:p></span></font></p>
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<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>all the best! please let us know your choice of solution!<o:p></o:p></span></font></p>
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<p class=MsoNormal style='margin-bottom:12.0pt'><font size=3
face="Times New Roman"><span style='font-size:12.0pt'><br>
<br>
<o:p></o:p></span></font></p>
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<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>On Mon, Mar 5, 2012 at 3:29 PM, Joaquin Rapela <<a
href="mailto:rapela@ucsd.edu">rapela@ucsd.edu</a>> wrote:<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'>Dear Aleksandra,<br>
<br>
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.<br>
<br>
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.<br>
<br>
Cordially, Joaquin<o:p></o:p></span></font></p>
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<p class=MsoNormal style='margin-bottom:12.0pt'><font size=3
face="Times New Roman"><span style='font-size:12.0pt'><br>
On Mon, Mar 05, 2012 at 09:41:08PM +0000, <st1:PersonName w:st="on">Aleksandra
Vuckovic</st1:PersonName> wrote:<br>
> Dear all,<br>
> 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?<br>
> Many thanks,<br>
> Aleksandra<o:p></o:p></span></font></p>
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