[FieldTrip] ICA+frqanalysis questions

Fri May 27 06:32:01 CEST 2011

Dear Yuval and discussion group,

it seems to me that what you are proposing is getting close to what proposed
by the hybrid approach of regica described here:

*Manousos A. Klados, Christos Papadelis, Christoph Braun, Panagiotis D.
Bamidis, REG-ICA: A hybrid methodology combining Blind Source Separation and
regression techniques for the rejection of ocular artifacts, Biomedical
Signal Processing and Control, In Press, Corrected Proof, Available online
16 March 2011, ISSN 1746-8094, DOI: 10.1016/j.bspc.2011.02.001.*

They suggest to selectively run regression based AR only on those components
which correlate with EOG signals. This makes sense to me and I have been
trying to experiment that on some old data, although with no clear
conclusions yet. It may be worth a try for Odelia: Anybody out there with
some insights for this - or maybe a similar - approach?

Cheers,

Mahesh

Mahesh M. Casiraghi
PhD candidate - Cognitive Sciences
Roberto Dell'Acqua Lab, University of Padova
Pierre Jolicoeur Lab, Univesité de Montréal
mahesh.casiraghi at umontreal.ca

I have the conviction that when Physiology will be far enough advanced, the
poet, the philosopher, and the physiologist will all understand each other.
Claude Bernard

On Thu, May 26, 2011 at 11:40 PM, Yuval Harpaz <yuvharpaz at gmail.com> wrote:

> 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.
>>>
>>>
>>>  _______________________________________________
>>>
>>> fieldtrip mailing list
>>>
>>>  fieldtrip at donders.ru.nl
>>>
>>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>>
>>>
>>>
>>>
>>> --
>>> David Groppe, Ph.D.
>>> Postdoctoral Researcher
>>> Kutaslab
>>> Dept. of Cognitive Science
>>> University of California, San Diego
>>> http://www.cogsci.ucsd.edu/~dgroppe/
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
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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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>>
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>
>
>
> --
> Y.Harpaz
>
> a link to the BIU MEG lab:
> http://faculty.biu.ac.il/~goldsa/index.html
>
> *"Many were increasingly of the opinion that they'd all made a big mistake
> in coming down from the trees in the first place. And some said that even
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> *
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