[Eeglablist] filters, ICA and erp

Mon Oct 10 11:45:26 PDT 2011

Hi Sara,

In the paper that Steve mentioned we also looked at the effect of
filtering on the N1, and there was a much smaller impact of the
cut-off of the high pass filter.  This makes sense, given the much
higher frequency content of the early ERP signals.  However, it is
important to keep in mind that the impact of the filter on
low-frequency signals in your waveform can spread to other components,
therefore impacting even high frequency signals.  You can see this
effect in Figure 7 of the paper, where the impact of the filter on the
P3 is so strong that it spreads to the early ERPs as well.  I'm not
sure if the list allows attachments to go through, but I will send you
a copy of the paper to peruse!

Hope this helps!

-Emily

On Fri, Oct 7, 2011 at 4:10 PM, Sara Jane Webb <sjwebb at u.washington.edu> 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
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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
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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
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>>>> unsubscribe at sccn.ucsd.edu
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>>>>> to
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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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>>
>> --------------------------------------------------------------------
>> 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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-- 
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Emily S. Kappenman
UC Davis Center for Mind and Brain
267 Cousteau Place
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eskappenman at ucdavis.edu
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