[Eeglablist] filters, ICA and erp

Mon Oct 10 14:25:55 PDT 2011

Hi Emily,

Thanks for your reply. In your paper you mention using a Butterworth filter
with 24 dB/octave attenuation ... Do you recall the exact filter specs you
used?  I have never been able to design a satisfactory Butterworth filter in
matlab ...

I usually use the Parks-McClellan method (firpm.m in matlab) and then check
the frequency response using freqz.m, to verfity the sharpness at 1Hz and
the amount of attenuation in the stopband (filter is usually around 500-1000
taps long). I wonder if you would find the same degree of P3 attentuation,
and spurious peaks, using this kind of filter rather than Butterworth.
Perhaps you still have the test dataset? It would be nice to look at before
a final decree is made.

My suggestion to use 1 Hz is primarily in the interest of getting good ICA
components. If you get a good decomposition without filtering, that's great.
But there are some cases when filtering at 1Hz rather than 0.5 Hz, e.g., can
reduce nonstationarity further and produce better ICA components, so there
is potentially a tradeoff here.

Best,
Jason

-----Original Message-----
From: Emily Kappenman [mailto:eskappenman at ucdavis.edu] 
Sent: Monday, October 10, 2011 12:21 PM
To: japalmer at ucsd.edu
Cc: Steve Luck; eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] filters, ICA and erp

Hi Jason,

The filtering that lead to the effect on the P3 amplitude was actually done
on the continuous data.

Here is a link to the paper:
http://dl.dropbox.com/u/3661661/Kappenman%20Psychophysiology%202010.pdf

Hope this helps!
-Emily

On Fri, Oct 7, 2011 at 5:00 PM, Jason Palmer <japalmer29 at gmail.com> wrote:
> Steve,
>
>
>
> Thanks for the reference. It seems in this work you simulated ERP 
> epochs, and filtered the epochs?  I am referring to using a 1 Hz 
> filter (sharp, about 1024 taps) on the continuous data before 
> epoching. Do you find a similar difference in ERP amplitude in this case?
>
>
>
> Jason
>
>
>
> From: eeglablist-bounces at sccn.ucsd.edu 
> [mailto:eeglablist-bounces at sccn.ucsd.edu] On Behalf Of Steve Luck
> Sent: Thursday, October 06, 2011 8:25 PM
> To: eeglablist at sccn.ucsd.edu
>
> Subject: Re: [Eeglablist] filters, ICA and erp
>
>
>
> 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
>
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>
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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=A
> merica/Los_Angeles
>
> --------------------------------------------------------------------
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--
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Emily S. Kappenman
UC Davis Center for Mind and Brain
267 Cousteau Place
Davis, CA 95618
eskappenman at ucdavis.edu
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