[Eeglablist] filters, ICA and erp

[Stefan Debener]

Hi Sarah,
We usually filter the cnt data at 1hz, then epoch the cnt file into consecutive 1 sec dummy epochs, prune these epochs for non-stereotypical artifacts and apply ICA (extended infomax, no or very moderate dimensionality reduction with PCA) to the remaining (approx. 80-90%) data. The resulting unmixing weights nicely disentangle artifacts and can be applied to the unfiltered, or more ERP-appropriately filtered, cnt data. If your focus is on task-related ICs I'd recommend to submit epoched data reflecting event-related activity to ICA. Regarding muscle artifact it seems sometimes difficult to tease apart muscle from gamma from channel noise, i agree. Rene Scheeringa's recent Neuron paper (2011) demonstrates a smart way of getting gamma-related ICs.
Hope this helps,
Stefan

Am 06.10.2011 um 11:50 schrieb Sara Graziadio <sara.graziadio at newcastle.ac.uk>:

> 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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