[Eeglablist] filters, ICA and erp

Steve Luck sjluck at ucdavis.edu
Thu Oct 6 20:25:17 PDT 2011 

Jason and Sara-

A 1-Hz high-pass cutoff is very likely to dramatically reduce the amplitude of late components like P3 and N400.  To see an example of this, take a look at Figure 7 in Kappenman & Luck (2010, Psychophysiology), which shows the effects of various high-pass cutoffs on P3 amplitude.  Not only does a 1-Hz cutoff reduce peak amplitude by over 50%, it also creates a spurious negative-going peak at the beginning of the waveform.

I like David Groppe's suggestion of using epoched data with a fairly long epoch length and doing baseline correction as a type of high-pass filter.

Steve

> From: Jason Palmer <japalmer29 at gmail.com>
> Date: October 5, 2011 11:56:57 AM PDT
> To: 'Sara Graziadio' <sara.graziadio at newcastle.ac.uk>, <eeglablist at sccn.ucsd.edu>
> Subject: Re: [Eeglablist] filters, ICA and erp
> Reply-To: <japalmer at ucsd.edu>
> 
> 
> Hi Sara,
> 
> In my experience, using a sharp 1Hz high pass filter is best for ICA, and
> doesn't significantly reduce ERP amplitude--the ERPs I know of are at least
> 2 Hz, so the 1Hz high pass shouldn't be a problem. The main issue is to
> eliminate slow drifts in the data which make the mean non-stationary.
> 
> If you want to look at low frequencies specifically, you might do low pass
> filtering, or band pass between 0.1Hz and say 30 Hz, to try to remove high
> frequency sources, leaving only the low frequency sources, but I doubt this
> would improve ERP results over a ! Hz high-pass filter.
> 
> Average reference is also fine if you are doing ICA after. Spreading muscle
> artifacts etc. to other channels is not a problem since ICA will remove the
> muscle activity etc. and put it in a single source (usually).
> 
> After you do average reference, the data rank goes down by 1, so if you have
> 94 channels avg referenced, ICA should give you back 93 components/sources.
> 
> Hope this is helpful.
> 
> Jason
> 
> -----Original Message-----
> From: eeglablist-bounces at sccn.ucsd.edu
> [mailto:eeglablist-bounces at sccn.ucsd.edu] On Behalf Of Sara Graziadio
> Sent: Wednesday, October 05, 2011 7:46 AM
> To: eeglablist at sccn.ucsd.edu
> Subject: [Eeglablist] filters, ICA and erp
> 
> 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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> 
> 
> 
> 
> 
> From: Sara Graziadio <sara.graziadio at newcastle.ac.uk>
> Date: October 6, 2011 2:50:32 AM PDT
> To: 'David Groppe' <david.m.groppe at gmail.com>, "'japalmer29 at gmail.com'" <japalmer29 at gmail.com>
> Cc: "eeglablist at sccn.ucsd.edu" <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
>>> 
>>> 
>>> 
>>> _______________________________________________
>>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>>> To unsubscribe, send an empty email to eeglablist-
>> unsubscribe at sccn.ucsd.edu
>>> For digest mode, send an email with the subject "set digest mime" to
>> eeglablist-request at sccn.ucsd.edu
>>> 
>> 
>> 
>> 
>> --
>> 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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Steven J. Luck, Ph.D.
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