Yes, the comment of Ronald (below) is correct: Running ICA a second time on the back-projection of an IC subset (or on the IC subset waveforms themselves) will 1) need to be a lower-dimension decomposition (in the first case using PCA data dimension reduction on the channel data; in the second, on the reduced number of IC waveforms directly), and 2) should return the same results (IC maps and time courses) as the original decomposition (within numeric/statistical error bounds). <br>
<br>Running ICA on data from which time points at which non-stereotyped 'noise' epochs or stretches of data have been removed, on the other hand, can indeed result in a better ICA decomposition -- in two senses: a) the resulting IC time courses will be more independent, and b) more of the ICs produced will have a ('dipolar') scalp map taht can be associated with activity in a single (localizable) cortical patch...<br>
<br>We will soon include in EEGLAB a plug-in function by Jason Palmer to measure numerically the reduction in mutual information effected by an ICA decomposition (i.e., info on measure a) above); the number of 'dipolar' ICs returned by a given decomposition can be determined now by properly fitting equivalent dipole models to the IC scalp maps, and measuring the number that are fit with low residual variance...<br>
<br>Scott Makeig<br><br><br><br><div class="gmail_quote">On Tue, Aug 16, 2011 at 12:15 AM, Ronald Phlypo <span dir="ltr"><<a href="mailto:Ronald.Phlypo@ugent.be">Ronald.Phlypo@ugent.be</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div bgcolor="#FFFFFF" text="#000000">
Dear Max,<br>
<br>
the problem lies in the maximally allowed number of components in
both decompositions. The first decomposition may allow for as many
sources as sensors (15 in your case). However, once 3 artefacts have
been removed, your data dimension reduces to 12, which allows to
estimate a maximum of 12 sources only. To circumvent this problem, I
suppose what the wiki means is to do local decompositions first
(trial by trial or epoch by epoch) and then concatenating the
trials/epochs again after their correction and before the second
"joint" decomposition. Since in this case the artefact removal is
nonlinear, it does not reduce the dimension of your concatenated
data.<br>
<br>
Hope this helps,<br><font color="#888888">
<br>
Ronald<br>
</font><br>
PS: you might also want to have a look at <a href="http://www.hindawi.com/journals/cin/2007/075079/cta/" target="_blank">http://www.hindawi.com/journals/cin/2007/075079/cta/</a>
where short time and long term windows are used jointly for artefact
removal. The text refers to literature on the mean duration of
electrophysiological processes to motivate this decision.<br>
<br>
Le 15/08/2011 18:09, Maximilien Chaumon a écrit :
<blockquote type="cite"><div><div></div><div class="h5">Hello all,<br>
<br>
I'm currently cleaning data before working with components.<br>
<ul>
<li>I cut my dataset into epochs</li>
<li>reject epochs where signal is bad</li>
<li>run an ICA</li>
<li>find blink and muscle components, reject them</li>
<li>run an ICA again, and look at my components.</li>
</ul>
... as I understood was suggested at the bottom of this page <a href="http://sccn.ucsd.edu/wiki/Chapter_01:_Rejecting_Artifacts" target="_blank">http://sccn.ucsd.edu/wiki/Chapter_01:_Rejecting_Artifacts</a><br>
<br>
Then the ICs look very nice but come in pairs of extremely similar
topographies with different time courses, as shown on <a href="http://oszilla.hgs.hu-berlin.de/public/2ICAs.png" target="_blank">this picture</a>.<br>
I am wondering what happened here. I can imagine that rejecting
components before running the second ICA is what went wrong... But
why did I read that on the wiki?<br>
<br>
Thanks a lot for any advice.<br>
Max<br>
<br>
<br>
<fieldset></fieldset>
<br>
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