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<div class="moz-cite-prefix">Hi Cyril,<br>
<br>
<br>
On 13.5.2014 21:44, Dr Cyril Pernet wrote:<br>
</div>
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<p> Hi Makoto & Michal,<br>
<br>
I agree with Makoto about the ICA subspace which can be quite
different - there is however another thing to consider<br>
You said ' Should the effect be stronger (in terms of more
statistically significant electrodes (dipoles) and timeperiods)
on scalp electrodes or in DIPFIT clusters?'<br>
<br>
the problem here is that statistically significant is an
estimate under H0, so beside the hypothesis test, you cannot
tell if the effect is stronger or weaker in one case or the
other because a p value tells nothing about H1 -- to do that you
need to look at the actual effect size (like what is the mean uV
difference between conditions) and not base your judgment the
(correted) p values. </p>
</blockquote>
It is correct from the statistical point of view. I was looking for
some "common sense" interpretation, but I know that there are some
limitations of direct comparison<br>
<br>
<blockquote
cite="mid:20140513204401.1557349c4mueensw@www.staffmail.ed.ac.uk"
type="cite">
<p>You could also test if the effects are different using a test
for apparied measures (eg. a paired t-test between (condition A
- condition B) on one compoment vs (A -B) on one channel).<br>
<br>
</p>
</blockquote>
Thanks for advice<br>
<br>
MIchal<br>
<br>
<blockquote
cite="mid:20140513204401.1557349c4mueensw@www.staffmail.ed.ac.uk"
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<p> Cyril<br>
<br>
--------------------------------<br>
Dear Michal, </p>
<div> </div>
<div> That's a simple but deep question. </div>
<div> Theoretically the difference between condition can't be
smaller in ICA recults since canceling happens in the mixing
process and not the other way around (like the law of entropy?)
</div>
<div> </div>
<div> However, I believe a major problem in comparing channels
with ICs is component selection. The question is how you
guarantee that the ICs you choose is a right representative
(projecting source) to the channel? What if some subject don't
have such ICs? What if some subjects have multiple of such ICs
(subspace)? </div>
<div> </div>
<div> One way to investigate this problem is run pvaf analysis
(you have pvaftopo under EEGLAB plugin manager) </div>
<div> I have an experience of computing the pvaf analysis across
subjects per cluster (unpublished data), and the result showed
very large standard deviations... it was like mean 30% and
SD=30, range 5-80. This means a cluster can explain a channel
activity (in my result, of course) only by 30%, and there are
huge inter-subject variance. </div>
<div> </div>
<div> This being said, I think it is still ok to stay optimistic
and take the theoretical conclusion. You haven't observed
horrendously contradicting results, have you? </div>
<div> </div>
<div> Makoto </div>
<div> </div>
<p> 2014-05-12 14:02 GMT-07:00 Michal Vavrecka <span dir="ltr"><<a
moz-do-not-send="true" target="_blank">vavrecka@fel.cvut.cz</a>></span>:<br>
Hello,<br>
<br>
I do have few simple questions and I am curious about your
intuitions and arguments:<br>
<br>
I am finishing the paper where I did group analysis of two
cognitive states. I visualized both scalp maps and dipoles and
their statistical tests. Both visualization are based on
fieldtrip monte carlo permutation with cluster based statistics
(correction for multiple comparison). I would like to interpret
the difference between results on the scalp and inside the brain
(DIPFIT). What are your intuitions:<br>
<br>
Should the effect be stronger (in terms of more statistically
significant electrodes (dipoles) and timeperiods) on scalp
electrodes or in DIPFIT clusters?<br>
<br>
How to interpret the stronger effect on the scalp?<br>
Does the ICA and DIPFIT calculation somehow weaken the ERSP
difference?<br>
My intuition is opposite as the source reconstruction has to
clean the noise and strengthen the effect that should result in
more statistically significant timeperiods in the spectrograms
compared to scalp data?<br>
Is there any paper that compares these two approaches?<br>
<br>
Thanks for your answers.<br>
<br>
Michal<br>
</p>
<p> --<br>
Dr Cyril Pernet,<br>
Academic Fellow<br>
Brain Research Imaging Center<br>
Neuroimaging Sciences<br>
University of Edinburgh<br>
<br>
Western General Hospital<br>
Division of Clinical Neurosciences<br>
Crewe Road<br>
Edinburgh<br>
EH4 2XU<br>
Scotland, UK<br>
<br>
<a class="moz-txt-link-abbreviated" href="mailto:cyril.pernet@ed.ac.uk">cyril.pernet@ed.ac.uk</a><br>
tel: +44(0)1315373661<br>
<a moz-do-not-send="true" target="_blank"
href="http://www.sinapse.ac.uk/">http://www.sinapse.ac.uk/</a><br>
<a moz-do-not-send="true" target="_blank"
href="http://www.sbirc.ed.ac.uk/cyril">http://www.sbirc.ed.ac.uk/cyril</a>
</p>
<p> <br>
<br>
--<br>
The University of Edinburgh is a charitable body, registered in<br>
Scotland, with registration number SC005336.<br>
</p>
<br>
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<br>
<br>
<pre class="moz-signature" cols="72">--
Michal Vavrecka
assistant professor
Biodat Research Group
Incognite Research Unit
FEE CTU
Karlovo nam. 13
Prague 2
phone: +420224357609
cell: +420608661977
personal: <a class="moz-txt-link-freetext" href="http://bio.felk.cvut.cz/~vavrecka/">http://bio.felk.cvut.cz/~vavrecka/</a>
groups: <a class="moz-txt-link-freetext" href="http://incognite.felk.cvut.cz/">http://incognite.felk.cvut.cz/</a>
<a class="moz-txt-link-freetext" href="http://bio.felk.cvut.cz/">http://bio.felk.cvut.cz/</a></pre>
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