[Eeglablist] filters, ICA and erp

[Rey Ramirez]

You may want to consider running ICA on the 1 Hz high-pass data, but then
obtaining the activation time series from the 0.1 Hz high-pass data.
act_pt1Hz = unmix_1Hz * data_pt1Hz    (where pt1Hz refers to the 0.1 Hz
high-passed, and unmix_1Hz=wts_1Hz*sph_1Hz is the unmixing matrix for the
1Hz high-pass).

Rey

On Thu, Oct 6, 2011 at 8:25 PM, Steve Luck <sjluck at ucdavis.edu> 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
>
>
>
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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
>
>
>
>
> _______________________________________________
>
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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
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-- 
Rey R. Ramírez, Ph.D.
MEG Brain Imaging Center
Institute for Learning & Brain Sciences
Portage Bay Building
Box 357988
University of Washington
Seattle, WA 98195-7988
e-mail: rrramir at uw.edu
Tel: (206) 543-6484
Fax: (206) 221-6472
Cell: (619) 709-3590
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