[FieldTrip] ICA+frqanalysis questions

Fri May 27 05:40:14 CEST 2011

Dear discussion group
Did anybody consider smoothing or filtering the component trace before
rejecting it?
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?
yuval

On 27 May 2011 06:16, Joseph Dien <jdien07 at mac.com> wrote:

> 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 (
> http://sourceforge.net/projects/erppcatoolkit/).
>
> 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.
>
> Cheers!
>
> Joe
>
> On May 24, 2011, at 3:11 AM, Stefan Debener wrote:
>
>  Hi Odelia,
>
> 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...
>
> 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...
>
> Best,
> Stefan
>
>
> Am 5/24/11 4:00 AM, schrieb Alexander J. Shackman:
>
> And for a related perspective, see
>
> McMenamin, B. W., *Shackman, A. J.*, Greischar, L. L. & Davidson, R. J.
> (2011). Electromyogenic artifacts and electroencephalographic inferences
> revisited, *Neuroimage**, 54*, 4-9.
>
> http://psyphz.psych.wisc.edu/~shackman/mcmenamin_shackman_ni2011.pdf
>
>
> On Mon, May 23, 2011 at 8:07 PM, Joseph Dien <jdien07 at mac.com> wrote:
>
>>  I agree with David's reasoning.  You may find the following article to
>> be of help as well in understanding the issues involved:
>>
>>  Dien, J., Khoe, W., & Mangun, G. R. (2007). Evaluation of PCA and ICA of
>> simulated ERPs: Promax versus Infomax rotations. *Human Brain Mapping*,
>> 28(8), 742-763.
>>
>>  Cheers!
>>
>>  Joe
>>
>>  On May 23, 2011, at 11:57 AM, David Groppe wrote:
>>
>>  Hi Odelia,
>>   When you use ICA (or any other spatial filter) to correct for EEG
>> artifacts, you're going to distort your data some by removing true EEG
>> activity in addition to the artifact (for an explanation, see:
>> http://www.cogsci.ucsd.edu/%7Edgroppe/PUBLICATIONS/GroppeCSO2008.pdf).
>> So to minimize distortion, it would be better not to apply ICA
>> artifact correction to artifact-free data.  However, if the frequency
>> of the artifact differs across experimental conditions, it could
>> confound your analysis.  For example, I suspect people blink more
>> often to targets in an oddball experiment than standards.  Thus if you
>> apply ICA only to blinky trials, you could find a difference between
>> the EEG response to standards and targets that simply reflects the
>> fact ICA removed more EEG activity in the target trials (i.e., it
>> wouldn't reflect a true difference in neural processing).
>>      hope this helps,
>>         -David
>>
>> On Mon, May 23, 2011 at 1:44 AM, odelia nakar <odidodi at hotmail.com>
>> wrote:
>>
>> Hi all,
>>
>>
>>  I'm troubled by the fact that when I use ICA for blinks\eyes movements
>>
>>  removal, I remove the relevant components also from trials that do not
>>
>> contain blinks\eyes movements. In order to avoid this bias we thought to
>>
>>  combine the data before ICA ("data" structure) with the data after ICA
>>
>> ("dataica" structure), only in specific trials, as follows:
>>
>>
>>  datall=dataica;
>>
>> datall.trial=data.trial;
>>
>> datall.time=data.time;
>>
>>  blinks=[2 4 5 8 bla bla 156];
>>
>> for ind=1:length(blinks)
>>
>>      datall.trial{1,blinks(ind)}=dataica.trial{1,blinks(ind)};
>>
>>  end
>>
>>
>>  To my first question: I just wanted to check that there is no problem
>> with
>>
>> that, or any reason not to use it.
>>
>>
>>  Another issue- I use motor learning task, and I'm trying to understand
>> what
>>
>> happens through the process, in terms of power-frequency changes through
>> the
>>
>> process. How would you recommend that I'd use the ft_freqanalysis
>> function?
>>
>> What method to use (or what do I need to consider when choosing the method
>>
>> field)?
>>
>>
>>
>>  Thanks a lot,
>>
>>  Odelia.
>>
>>
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>>
>>
>>
>>
>> --
>> David Groppe, Ph.D.
>> Postdoctoral Researcher
>> Kutaslab
>> Dept. of Cognitive Science
>> University of California, San Diego
>> http://www.cogsci.ucsd.edu/~dgroppe/
>>
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>>
>>
>>
>> --------------------------------------------------------------------------------
>>
>>  Joseph Dien
>> E-mail: jdien07 at mac.com
>> Phone: 301-226-8848
>> Fax: 301-226-8811
>> http://homepage.mac.com/jdien07/
>>
>>
>>
>>
>>
>>
>>
>>
>>
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>>
>
>
> --
> Alexander J. Shackman, Ph.D.
> Wisconsin Psychiatric Institute & Clinics and
> Department of Psychology
> University of Wisconsin-Madison
> 1202 West Johnson Street
> Madison, Wisconsin 53706
>
> Telephone: +1 (608) 358-5025
> Fax: +1 (608) 265-2875
> Email: shackman at wisc.edu
> http://psyphz.psych.wisc.edu/~shackman
>
>
> _______________________________________________
> fieldtrip mailing listfieldtrip at donders.ru.nlhttp://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
>
> --
> Prof. Dr. Stefan Debener
> 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
> Email: stefan.debener at uni-oldenburg.de
>
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