[Eeglablist] filters, ICA and erp

Fri Oct 7 16:10:25 PDT 2011

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

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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
>> For digest mode, send an email with the subject "set digest mime" to
>> eeglablist-request at sccn.ucsd.edu
>>
>>
>>
>>
>>
>> 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
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