[Eeglablist] filters, ICA and erp

Thu Oct 13 07:20:40 PDT 2011

Steve,
actually I was refering to your book when I was writing that the filter would deforme/reduce the erp. But following David Groppe's suggestion would mean to reduce activity at different frequency all across the spectrum, wihtout exactly knowing which frequencies I am reducing, am I right? If I want to look at the psd as well as at the erps, would this analysis just be correct? I am always concerned about applying data modification that I cannot fully control..if you know what I mean...
Thank you very much
Best

Sara

________________________________
From: eeglablist-bounces at sccn.ucsd.edu [eeglablist-bounces at sccn.ucsd.edu] On Behalf Of Steve Luck [sjluck at ucdavis.edu]
Sent: 07 October 2011 04:25
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<mailto:japalmer29 at gmail.com>>
Date: October 5, 2011 11:56:57 AM PDT
To: 'Sara Graziadio' <sara.graziadio at newcastle.ac.uk<mailto:sara.graziadio at newcastle.ac.uk>>, <eeglablist at sccn.ucsd.edu<mailto:eeglablist at sccn.ucsd.edu>>
Subject: Re: [Eeglablist] filters, ICA and erp
Reply-To: <japalmer at ucsd.edu<mailto: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>
[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<mailto: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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--
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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