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<DIV><FONT face=Arial size=2>Hi,</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>I'm studying some statistics at school and trying
to better understand EEGLAB's methodology better. As I read through the
clustering tutorial (<A
href="http://sccn.ucsd.edu/eeglab/clusttut/clustertut.html">http://sccn.ucsd.edu/eeglab/clusttut/clustertut.html</A>),
there are some things I'm not sure about.</FONT></DIV>
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<DIV><FONT face=Arial size=2>1. It seems that the ERPs are limited by N
dimensions (just like all components used in clustering), and the tutorial says
that PCA is used to limit the dimensions. However, the ERPs are really ICA
components; is it necessary to do PCA at all? After ICA you have a list of
components ordered by the variance they account for, right? I have a
feeling I'm not reading this part of the tutorial right; can somebody enlighten
me please? :)</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>2. The different measures used for clustering have
different metrics, so normalization seems smart. I was expecting some kind
of z-scoring; however, the tutorial says that for each set of measurements
(spectra, ERP, dipoles, etc) used in the clustering, the components are all
divided by the standard deviation of the PCA component that accounts for the
largest amount of variance. Could somebody help me understand why that
computation was used? i'm not good enough analytically with the math yet
to try and derive it on paper...</FONT></DIV>
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<DIV><FONT face=Arial size=2>Thank you so much in advance!<BR>Ben
Cipollini</FONT></DIV></BODY></HTML>