[Eeglablist] filters, ICA and erp

Steve Luck sjluck at ucdavis.edu
Fri Oct 7 16:52:55 PDT 2011 

Hi Sara.  I haven't systematically looked at the effects of high-pass filters on earlier components with real data.  However, there is an extensive discussion of the general issue of filter-induced distortions in chapter 5 of my book on ERP methods (An Introduction to the Event-Related Potential Technique, MIT Press).  See Figure 5.10 for examples with artificial data.

Here's the issue in a nutshell: Any transient ERP will contain a broad spectrum of frequencies, so even a "high frequency" component like P1 has lots of low frequencies in it.  A high-pass filter will tend to induce opposite-polarity artificial peaks at the beginning and end of the waveform, and can also impact the peak amplitude.

The fundamental principle is that there is an inverse relationship between precision in the time domain and precision in the frequency domain.  If you use a filter to restrict the information in the frequency domain (essentially increasing precision in the frequency domain), you will necessarily spread the information out in the time domain (decrease precision in the time domain).  Since the most important virtue of ERPs is their temporal resolution, and filters decrease temporal precision, filters actually reduce the very thing that is most compelling about ERPs.  Thus, filters should be used sparingly.

Steve


On Oct 7, 2011, at 4:10 PM, Sara Jane Webb wrote:

> Hi Steve et al.,
> 
> Have you looked at amplitude attenuation when using a highpass of 1Hz on earlier signals like the P1?
> 
> Thanks,
> 
> Sara
> 
> Sara Jane Webb, PhD
> Associate Professor of Psychiatry and Behavioral Sciences
> Autism Research Program
> http://depts.washington.edu/pbslab/
> Box 357920; CHDD 314C; University of Washington
> Seattle WA 98195
> 206.221.6461
> sjwebb at u.washington.edu
> 
> Confidentiality Notice:  Because email is not secure, please be aware that we cannot guarantee the confidentiality of information sent by email.  If you are not the intended recipient, please notify the sender by reply email, and then destroy all copies of the message and any attachments.
> 
> On Oct 6, 2011, at 8:25 PM, Steve Luck wrote:
> 
>> 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
>>> 
>>> 
>>> 
>>> _______________________________________________
>>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>>> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.ucsd.edu
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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.
>> Director, Center for Mind & Brain
>> Professor, Department of Psychology
>> University of California, Davis
>> Room 109
>> 267 Cousteau Place
>> Davis, CA 95618
>> (530) 297-4424
>> E-Mail: sjluck at ucdavis.edu
>> Web: http://mindbrain.ucdavis.edu/people/sjluck
>> Calendar: http://www.google.com/calendar/embed?src=stevenjluck%40gmail.com&ctz=America/Los_Angeles
>> --------------------------------------------------------------------
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> _______________________________________________
>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
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> 

--------------------------------------------------------------------
Steven J. Luck, Ph.D.
Director, Center for Mind & Brain
Professor, Department of Psychology
University of California, Davis
Room 109
267 Cousteau Place
Davis, CA 95618
(530) 297-4424
E-Mail: sjluck at ucdavis.edu
Web: http://mindbrain.ucdavis.edu/people/sjluck
Calendar: http://www.google.com/calendar/embed?src=stevenjluck%40gmail.com&ctz=America/Los_Angeles
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