[Eeglablist] filters, ICA and erp

[Scott Makeig]

Sara,   Given enough channels (i.e., deg of freedom), ICA separates out each
scalp/neck muscle activity -- Note there may be information of interest in
their independent activities as well! Therefore, EEG montages including low
electrodes need relatively high density there, since the spatial source
(muscle) density is high around the insertions of the neck muscles into the
skull (this is where the EMG activity appears on the scalp, with a polarity
reversal in the direction of the muscle and centered at the muscle end).
 This is quite likely why in your experience muscle activity "usually ...
spreads on half of the components," as you say.

Scott Makeig

On Thu, Oct 6, 2011 at 2:50 AM, Sara Graziadio <
sara.graziadio at newcastle.ac.uk> wrote:

> 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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> >
> >
> >
> >--
> >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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-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation; Prof. of
Neurosciences (Adj.), University of California San Diego, La Jolla CA
92093-0559, http://sccn.ucsd.edu/~scott
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