[Eeglablist] filters, ICA and erp

Fri Oct 7 05:58:35 PDT 2011

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 the 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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