<div dir="ltr"><div>Dear discussion group</div>Did anybody consider smoothing or filtering the component trace before rejecting it?<br>it seems that the added noise to no-blink trials is in a frequency higher than that typical to blinks. what if we evaluate the component weight, creating a trace for the eyeblink component for every trial, then bandpass filter the blink trace , say 0.1-25Hz, and only then remove the component from the data?<div>
yuval</div><div><br></div><div><div class="gmail_quote">On 27 May 2011 06:16, Joseph Dien <span dir="ltr"><<a href="mailto:jdien07@mac.com">jdien07@mac.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
<div style="word-wrap:break-word"><div>Stefan, just to be clear, I don't think any of us were saying not to use ICA to correct blinks. David was just saying that there are potential concerns when one only applies the ICA to the blink trials rather than to all the trials. I myself use EEGlab's infomax implementation in the automatic eyeblink correction tool of my EP Toolkit (<a href="http://sourceforge.net/projects/erppcatoolkit/" target="_blank">http://sourceforge.net/projects/erppcatoolkit/</a>).</div>
<div><br></div><div>Now that said, I should add a little more nuance to my response. One of the things I observed (or rather, that Tim Curran pointed out to me) is that when you apply ICA to remove eyeblink artifacts in this manner, it can actually substantially increase the noise level in the data, so for the trials without eyeblinks it can have a considerable cost. So in order to balance the cost/benefit ratio, what I did was to include a trial by trial criterion that the putative eyeblink factors would only be removed if doing so reduced the overall variance of the trial. This approach does still have some potential for causing the concerns that David raises but not as much as only applying the ICA to blink trials since it does end up getting applied to non-blink trials too. This does mean that one should be cautious about any apparent effects in the artifact corrected data that are centered around the eyes (that have a blink topography) but that goes without saying in any case. So anyway, I agree, it's not perfect but seems to be the best available option.</div>
<div><br></div><div>Cheers!</div><div><br></div><div>Joe</div><div><div></div><div class="h5"><br><div><div>On May 24, 2011, at 3:11 AM, Stefan Debener wrote:</div><br><blockquote type="cite">
<div bgcolor="#ffffff" text="#000000">
Hi Odelia,<br>
<br>
I have a slightly different opinion here. It is certainly true that
any filter has the tendency to distort data (with distortion I mean
that data consists of a mixture of some wanted, true signal and some
unwanted signal, and that the removal of the unwanted part of the
signal is neither complete nor specific). In our lab we regularly
use ICA for artefact removal (and more), and the benefit/gain is
clearly are much larger than the distortion. In fact there are a
number of examples out showing that currently only ICA (or related
tools) can recover the study of (a substantial fraction of the
wanted) EEG signal (but again, it is NOT a perfect tool at all), in
particular in cases where other means of SNR enhancement don't work
well (averaging, spectral analysis). I am happy to provide
references if you are interested...<br>
<br>
For the evaluation of outcome it would be reasonable to not evaluate
the ERP alone, as this could be misleading. Better evaluate the
sensitivity and specificity of an eye blink attentuation approach on
the single trial (and single subject) level, this will give you good
insight. And it is worth keeping in mind that the preprocessing of
the data (among other issues, like the quality of the recording and
so on) largely determines the quality of the output (for some
introduction you may look up chapter 3.1 in Ullsperger &
Debener, 2010, Simultaneous EEG and FMRI, Oxford University Press).
Just by a different preprocessing ICA output could vary between crap
and excellent unmixing. Thus a poor ICA eye blink attenuation would
make me a bit suspicious...<br>
<br>
Best,<br>
Stefan<br>
<br>
<br>
Am 5/24/11 4:00 AM, schrieb Alexander J. Shackman:
<blockquote type="cite">
And for a related perspective, see <br>
<br>
<span><span style="font-size:11pt;color:black">McMenamin</span></span><span style="font-size:11pt;color:black">, B.
W., <b>Shackman, A. J.</b>, <span>Greischar</span>,
L. L. & Davidson, R. J. (2011). <span>Electromyogenic</span>
artifacts and electroencephalographic inferences revisited, <span><b><i>Neuroimage</i></b></span><i>, 54</i>, 4-9. <br>
<br>
<a href="http://psyphz.psych.wisc.edu/%7Eshackman/mcmenamin_shackman_ni2011.pdf" target="_blank">http://psyphz.psych.wisc.edu/~shackman/mcmenamin_shackman_ni2011.pdf</a><br>
<br>
</span><br>
<div class="gmail_quote">On Mon, May 23, 2011 at 8:07 PM, Joseph
Dien <span dir="ltr"><<a href="mailto:jdien07@mac.com" target="_blank">jdien07@mac.com</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 style="word-wrap:break-word">
<div>I agree with David's reasoning. You may find the
following article to be of help as well in understanding
the issues involved:</div>
<div><br>
</div>
<div>
<div style="margin:0px">Dien, J., Khoe, W., &
Mangun, G. R. (2007). Evaluation of PCA and ICA of
simulated ERPs: Promax versus Infomax rotations. <i>Human
Brain Mapping</i>, <span style="font:13px 'Lucida Grande'">28</span><span style="font:12px 'Lucida Grande'">(</span><span style="font:13px 'Lucida Grande'">8</span>), <span style="font:13px 'Lucida Grande'">742-763</span>.</div>
</div>
<div><br>
</div>
<div>Cheers!</div>
<div><br>
</div>
<div>Joe</div>
<br>
<div>
<div>On May 23, 2011, at 11:57 AM, David Groppe wrote:</div>
<br>
<blockquote type="cite">
<div>Hi Odelia,<br>
When you use ICA (or any other spatial filter) to
correct for EEG<br>
artifacts, you're going to distort your data some by
removing true EEG<br>
activity in addition to the artifact (for an
explanation, see:<br>
<a href="http://www.cogsci.ucsd.edu/%7Edgroppe/PUBLICATIONS/GroppeCSO2008.pdf" target="_blank">http://www.cogsci.ucsd.edu/%7Edgroppe/PUBLICATIONS/GroppeCSO2008.pdf</a>).<br>
So to minimize distortion, it would be better not to
apply ICA<br>
artifact correction to artifact-free data. However,
if the frequency<br>
of the artifact differs across experimental
conditions, it could<br>
confound your analysis. For example, I suspect people
blink more<br>
often to targets in an oddball experiment than
standards. Thus if you<br>
apply ICA only to blinky trials, you could find a
difference between<br>
the EEG response to standards and targets that simply
reflects the<br>
fact ICA removed more EEG activity in the target
trials (i.e., it<br>
wouldn't reflect a true difference in neural
processing).<br>
hope this helps,<br>
-David<br>
<br>
On Mon, May 23, 2011 at 1:44 AM, odelia nakar <<a href="mailto:odidodi@hotmail.com" target="_blank">odidodi@hotmail.com</a>>
wrote:<br>
<blockquote type="cite">Hi all,<br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite">I'm troubled by the fact that
when I use ICA for blinks\eyes movements<br>
</blockquote>
<blockquote type="cite">
removal, I remove the relevant components also from
trials that do not<br>
</blockquote>
<blockquote type="cite">contain blinks\eyes movements.
In order to avoid this bias we thought to<br>
</blockquote>
<blockquote type="cite">
combine the data before ICA ("data" structure) with
the data after ICA<br>
</blockquote>
<blockquote type="cite">("dataica" structure), only in
specific trials, as follows:<br>
</blockquote>
<blockquote type="cite">
<br>
</blockquote>
<blockquote type="cite">datall=dataica;<br>
</blockquote>
<blockquote type="cite">datall.trial=data.trial;<br>
</blockquote>
<blockquote type="cite">datall.time=data.time;<br>
</blockquote>
<blockquote type="cite">
blinks=[2 4 5 8 bla bla 156];<br>
</blockquote>
<blockquote type="cite">for ind=1:length(blinks)<br>
</blockquote>
<blockquote type="cite">
datall.trial{1,blinks(ind)}=dataica.trial{1,blinks(ind)};<br>
</blockquote>
<blockquote type="cite">
end<br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite">To my first question: I just
wanted to check that there is no problem with<br>
</blockquote>
<blockquote type="cite">that, or any reason not to use
it.<br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite">Another issue- I use motor
learning task, and I'm trying to understand what<br>
</blockquote>
<blockquote type="cite">happens through the process,
in terms of power-frequency changes through the<br>
</blockquote>
<blockquote type="cite">process. How would you
recommend that I'd use the ft_freqanalysis function?<br>
</blockquote>
<blockquote type="cite">What method to use (or what do
I need to consider when choosing the method<br>
</blockquote>
<blockquote type="cite">field)?<br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite">Thanks a lot,<br>
</blockquote>
<blockquote type="cite">
Odelia.<br>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<blockquote type="cite">_______________________________________________<br>
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<br>
<br>
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David Groppe, Ph.D.<br>
Postdoctoral Researcher<br>
Kutaslab<br>
Dept. of Cognitive Science<br>
University of California, San Diego<br>
<a href="http://www.cogsci.ucsd.edu/%7Edgroppe/" target="_blank">http://www.cogsci.ucsd.edu/~dgroppe/</a><br>
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</span></div>
</span></div>
</span></div>
</span></div>
</span></div>
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<br>
<br clear="all">
<br>
-- <br>
Alexander J. Shackman, Ph.D.<br>
Wisconsin Psychiatric Institute & Clinics and<br>
Department of Psychology<br>
University of Wisconsin-Madison<br>
1202 West Johnson Street<br>
Madison, Wisconsin 53706<br>
<br>
Telephone: +1 (608) 358-5025<br>
Fax: +1 (608) 265-2875<br>
Email: <a href="mailto:shackman@wisc.edu" target="_blank">shackman@wisc.edu</a><br>
<a href="http://psyphz.psych.wisc.edu/%7Eshackman" target="_blank">http://psyphz.psych.wisc.edu/~shackman</a><br>
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<br>
<pre cols="72">--
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Neuropsychology Lab
Department of Psychology
University of Oldenburg
D-26111 Oldenburg
Germany
Office: A7 0-038
Phone: +49-441-798-4271
Fax: +49-441-798-5522
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