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<div class="moz-cite-prefix">Dear Makoto,<br>
<br>
thanks for your answer.<br>
<br>
<br>
On 13.5.2014 03:14, Makoto Miyakoshi wrote:<br>
</div>
<blockquote
cite="mid:CAEqC+SWJPcuoX0PkfMTz_qa5vcODYCtbUw-q4gpZxfeM+q1KRQ@mail.gmail.com"
type="cite">
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<div dir="ltr">Dear Michal,
<div><br>
</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><br>
</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><br>
</div>
</div>
</blockquote>
This is good argument.<br>
<blockquote
cite="mid:CAEqC+SWJPcuoX0PkfMTz_qa5vcODYCtbUw-q4gpZxfeM+q1KRQ@mail.gmail.com"
type="cite">
<div dir="ltr">
<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subjects per cluster (unpublished data), and the result showed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>
</blockquote>
I am going to check this functions in near future<br>
<br>
<blockquote
cite="mid:CAEqC+SWJPcuoX0PkfMTz_qa5vcODYCtbUw-q4gpZxfeM+q1KRQ@mail.gmail.com"
type="cite">
<div dir="ltr">
<div><br>
</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>
</blockquote>
No, the results are ok, I am just thinking how to interpret the
smaller effect (significant periods) in DIPFIT results. <br>
<br>
Michal<br>
<br>
<blockquote
cite="mid:CAEqC+SWJPcuoX0PkfMTz_qa5vcODYCtbUw-q4gpZxfeM+q1KRQ@mail.gmail.com"
type="cite">
<div dir="ltr">
<div><br>
</div>
<div>Makoto</div>
<div><br>
</div>
<div class="gmail_extra">
<div class="gmail_quote">2014-05-12 14:02 GMT-07:00 Michal
Vavrecka <span dir="ltr"><<a moz-do-not-send="true"
href="mailto:vavrecka@fel.cvut.cz" target="_blank">vavrecka@fel.cvut.cz</a>></span>:<br>
<blockquote class="gmail_quote">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>
<br>
<br>
<br>
-- <br>
Michal Vavrecka<br>
assistant professor<br>
Biodat Research Group<br>
Incognite Research Unit<br>
FEE CTU<br>
Karlovo nam. 13<br>
Prague 2<br>
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</blockquote>
</div>
<br>
<br>
<div><br>
</div>
-- <br>
<div dir="ltr">Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University of California
San Diego<br>
</div>
</div>
</div>
</blockquote>
<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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