[Eeglablist] filters, ICA and erp

Mon Oct 10 12:56:39 PDT 2011

I'm looking forward to reading Arno's paper!

In the meantime, some may find our group's experiences using ICA to remove
EMG from the EEG useful:

http://psyphz.psych.wisc.edu/~shackman/mcmenamin_shackman_ni2011.pdf
and
http://psyphz.psych.wisc.edu/~shackman/mcmenamin_shackman_davidson_ni2010.pdf

Cheers,
Alex

On Fri, Oct 7, 2011 at 9:43 PM, Scott Makeig <smakeig at gmail.com> wrote:

> Does EMG in EEG data have to do with movements?
>
> Muscle tension is what generates EMG, not muscle movements per se. Two
> opposing muscles, when tensed together, may not produce movement, but do
> increase stiffness of the affected joint. Moving the body is only one of the
> two main things muscles do -- the other is to stiffen the body, e.g., to
> hold the head upright, but also for defensive and/or offensive purposes
> (actual or imagined). Thus I believe there is useful cognitive and emotional
> information in the configuration and strengths of neck muscle activations in
> situations when the head is not moving.
>
> Scott Makeig
>
> p.s. Arno is first author of a paper, long in preparation and now in final
> revision,  on differences between ICA algorithms applied to EEG data. We
> will share it with the list when finished-- I believe it
> has useful objective info on this subject.
>
> On Fri, Oct 7, 2011 at 5:58 AM, Sara Graziadio <
> sara.graziadio at newcastle.ac.uk> wrote:
>
>> Hello Alonso,
>> I agree that the subjects are not always moving and so the ICA does not
>> work perfectly, but this is still an issue as the cortical activity is
>> intermixed with the emg and if you remove that IC you are cleaning the data
>> on one side but you are removing some cortical activity on the other side.
>> And you don't know anything about where that cortical activity is coming
>> from, what it is and so on. If you study the cortico-muscular coherence for
>> example, removing channels with emgs will reduce your coherence. Almost in
>> all the ICs I obtain with fastica (that should be the best algorithm or at
>> least one of the best) I have also some cortical activity, you can see it if
>> you look at the full signals in time, sometimes it is also obvious from the
>> PSD of the IC. I think this is a very important limit of ICA that make me
>> dubious when I read in the articles that ICA was used for artefact
>> rejection. To which extend it is usually not clear. Probably more
>> information should be provided in th!
>>  e methods about this but nobody does it as far as I know. Perhaps
>> somebody with more experience than me could comment on this.
>>
>> The 0.5Hz limit is something my colleagues and I found and I think it is
>> the common experience for everybody, probably it was also previously
>> discussed in this forum. I am not sure if there are any references for this.
>> I don't think that the computer crashes because of your filter though, but
>> I am not the best person to reply to you on this.
>>
>> I am not sure this could help you though!
>>
>> Best
>>
>> Sara
>>
>>
>> >-----Original Message-----
>> >From: Alonso Valerdi, Luz M [mailto:lmalon at essex.ac.uk]
>> >Sent: 07 October 2011 12:44
>> >To: Sara Graziadio; 'David Groppe'; 'japalmer29 at gmail.com'
>> >Cc: eeglablist at sccn.ucsd.edu
>> >Subject: RE: [Eeglablist] filters, ICA and erp
>> >
>> >Hello Sara,
>> >
>> >I've been following your questions and replies and I have undergone the
>> >same experience that you are describing. The EMG seems to be mixed with
>> >another component, but over the time I started to believe that maybe the
>> >EMG doesn't come up over all the trial because the subject didn't move
>> all the
>> >time. Are you with me?
>> >
>> >On the other hand, I'd like to ask you why you posted previously that the
>> >high-pass filter below 0.5Hz is not suitable for ICA processing? Do you
>> have
>> >any reference to recommend me about this issue? The point is that I'm
>> >filtering my data between 0.1 - 40Hz, but the problem is that certain
>> datasets
>> >are stuck during the ICA processing and my computer crashes after several
>> >hours. I wonder if it is because of the filtering. Do you have any
>> comment?
>> >
>> >I'll really appreciate it!
>> >Cheers
>> >Luz
>> >
>> >-----Original Message-----
>> >From: eeglablist-bounces at sccn.ucsd.edu [mailto:eeglablist-
>> >bounces at sccn.ucsd.edu] On Behalf Of Sara Graziadio
>> >Sent: 06 October 2011 10:51
>> >To: 'David Groppe'; 'japalmer29 at gmail.com'
>> >Cc: eeglablist at sccn.ucsd.edu
>> >Subject: Re: [Eeglablist] filters, ICA and erp
>> >
>> >Hello,
>> >Thanks for your suggestion.
>> >
>> >As I was planning to do also a PSD analysis on the data I guess that to
>> remove
>> >the mean is not the best method if it works as a non-selective high pass
>> filter,
>> >am I right?
>> >
>> >I am applying the PCA before applying the ICA to reduce the number of
>> >components. How the data rank would be modified in this case?
>> >I have to admit that it never happened to me that the muscle artefact is
>> put in
>> >a single source with the ICA. Usually it spreads on half of the
>> components, is
>> >this only my experience?
>> >
>> >Thanks again
>> >
>> >Best wishes
>> >
>> >Sara
>> >
>> >
>> >>-----Original Message-----
>> >>From: David Groppe [mailto:david.m.groppe at gmail.com]
>> >>Sent: 05 October 2011 23:10
>> >>To: Sara Graziadio
>> >>Cc: eeglablist at sccn.ucsd.edu
>> >>Subject: Re: [Eeglablist] filters, ICA and erp
>> >>
>> >>Hi Sara,
>> >>   I found that a good way to improve the performance of ICA for ERP
>> >>analysis is to
>> >>1) Epoch your data into one or two second chunks time locked to the
>> >>event of interest
>> >>2) Remove the mean of each epoch at each channel
>> >>3) Run ICA to remove artifacts
>> >>4) Use a standard pre-event time window to baseline your data
>> >>5) Compute your ERPs
>> >>
>> >>Removing the mean of each epoch acts as a crude high-pass filter.
>> >>It's not nearly as selective as a "true" high pass filter but it
>> >>doesn't distort the ERP waveforms as much either.  Moreover we've
>> >>found that the procedure described above massively improves the
>> >>reliability of ICA when compared to standard ERP prestimulus
>> >>baselines:
>> >>
>> >>Groppe, D.M., Makeig, S., & Kutas, M. (2009) Identifying reliable
>> >>independent components via split-half comparisons. NeuroImage, 45
>> >>pp.1199-1211.
>> >>
>> >>Hope this helps,
>> >>       -David
>> >>
>> >>
>> >>
>> >>On Wed, Oct 5, 2011 at 10:46 AM, Sara Graziadio
>> >><sara.graziadio at newcastle.ac.uk> wrote:
>> >>> Hello,
>> >>> I would like just a suggestion about some data cleaning/analysis I am
>> doing.
>> >I
>> >>am doing an ERP analysis and I want to clean my data first with the ICA.
>> In
>> >>theory, though, I should not use an high-pass cutoff higher than 0.1 Hz
>> to not
>> >>reduce the erp amplitude. On the other side the ICA does not work well
>> if
>> >the
>> >>high-pass cutoff is lower than 0.5 Hz...what is then the best method to
>> >apply?
>> >>Has anybody tested how robust the ica is with a 0.1Hz filter?
>> >>> I have also another question: I am doing the analysis on 94 electrodes
>> >>referenced to Fz. I planned to average reference the data but actually
>> there
>> >is
>> >>quite a large spread of noise on all the electrodes with this method
>> >(muscular
>> >>artefacts for example from the temporal electrodes). But actually almost
>> all
>> >>the papers are using the average reference so I was surprised, am I the
>> only
>> >>one having this problem of noise? Would not be better just to keep the
>> Fz
>> >>reference and then perhaps to average the erps for every different
>> cortical
>> >>area and do the analysis on these averaged erps?
>> >>>
>> >>> Thank you very much
>> >>>
>> >>> Best wishes
>> >>>
>> >>> Sara Graziadio
>> >>> Research Associate
>> >>> Newcastle University
>> >>>
>> >>>
>> >>>
>> >>> _______________________________________________
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>> >>>
>> >>
>> >>
>> >>
>> >>--
>> >>David Groppe, Ph.D.
>> >>Postdoctoral Researcher
>> >>North Shore LIJ Health System
>> >>New Hyde Park, New York
>> >>http://www.cogsci.ucsd.edu/~dgroppe/
>> >
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>
>
>
> --
> Scott Makeig, Research Scientist and Director, Swartz Center for
> Computational Neuroscience, Institute for Neural Computation; Prof. of
> Neurosciences (Adj.), University of California San Diego, La Jolla CA
> 92093-0559, http://sccn.ucsd.edu/~scott
>
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-- 
Alexander J. Shackman, Ph.D.
HealthEmotions Research Institute | Lane Neuroimaging Laboratory
Wisconsin Psychiatric Institute & Clinics
University of Wisconsin-Madison
6001 Research Park Boulevard
Madison, Wisconsin 53719

Telephone: +1 (608) 358-5025
Fax: +1 (608) 265-2875
Email: shackman at wisc.edu
http://psyphz.psych.wisc.edu/~shackman
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