[Eeglablist] filters, ICA and erp

Fri Oct 14 00:43:13 PDT 2011

Hi Sara,

While it's good to be sceptical about software, only software will allow 
you to be objective and replicate your own results, as there are too 
many choices to be made between recording the data and the final result. 
We used to select ICs by visual inspection (of several IC properties) or 
based on a particular criterion (e.g. the IC contributes variance to an 
ERP in a particular latency range), but it's more objective and more 
efficient to be guided by software some cirterion (or a combination of 
criteria). To identify eye blink ICs, eye movement ICs and heart-beat 
artifact ICs we developed CORRMAP, which is avaibale as EEGLAB plug-in 
and now part of the official EEGLAB distribution (from version 10, I 
believe). It implements a semi-automatic identificartion of these 
components across all subjects, has been validated on 32, 64 and 
128-channel EEG data collected, successfully used in different labs, and 
performs favourably when compared to expert classification. The 
reference paper is:

Viola FC, Thorne J, Edmonds B, Schneider T, Eichele T, Debener S. 
(2009). Semi-automatic identification of independent components 
representing EEG artifact.
Clin Neurophysiol. 2009 May;120(5):868-77.

The CORRMAP documentation can be found here - but better use the 
official EEGLAB download version, Arno has made some recent changes:
http://www.debener.de/corrmap/corrmapplugin1.html

Note that CORRMAP works only if the maps (inverse weights) are 
consistent across subjects, which is not the case for any artifact (or 
brain-related IC). Accordingly, different problems require different 
software developments. The electrical artifact from cochlear implant 
users for instance needs to be removed for the analysis of AEPs, and 
this can be done with ICA. Here the IC maps within and across subjects 
are not spatially consistent (for instance due to positioning and type 
of the CI device, and more reasons), thus they need to be identified 
based on different criteria. The quality that can be obtained with ICA 
in these individuals is published here:

Viola FC, Thorne JD, Bleeck S, Eyles J, Debener S. (2011). Uncovering 
auditory evoked potentials from cochlear implant users with independent 
component analysis. Psychophysiology. 2011 Nov;48(11):1470-80.

We developed and validated an new EEGLAB plugin, CIAC (Cochelar Implant 
Artifact Correction), that performs at least as good as experts. The 
paper is currently in revision and we will make the software freely 
availabe after having the paper accepeted and the GUI coded.

On different ICA implementations: One informative, recent paper 
comparing 22 different algorithms is:
Klemm M, Haueisen J, Ivanova G. (2009). Independent component analysis: 
comparison of algorithms for the investigation of surface electrical 
brain activity.
Med Biol Eng Comput. 2009 Apr;47(4):413-23.

Unfortunately the paper is based on 16-channel EEG data, and believe the 
results cannot be easily generalized to high-density recordings. Every 
now and then we compare infomax with other ICA algorithms, but always 
end up using Iextended infomax, as this usually returns the best (that 
is, the most physiologically plausible) results.

Hope this helps,
Stefan

Am 10/13/11 4:08 PM, schrieb Sara Graziadio:
> Dear all,
> thank you for all the replies and discussions started on these topics. It was very useful for me.
> I have still some doubts though.
> Scott, when you mentioned to keep 128 ICs without using the PCA, this implies that then you has 128ICs to analyze and to select. I guess that you are proposing to use some software to recognize the good ICs. I am always sceptical to use software for this kind of judgements. Which software would you suggest? Did you compare software and human selection?
> Furthermore I was looking for some papers in which infomax and fastica were compared for IC separations possibly in different paradigms (like ERP analysis, PSD analysis, on continuous data) but I could not find any. Are you aware of any papers doing this? Or do you have any personal experience about it?
> Thank you very much
> Best
> Sara
> ________________________________
> From: Scott Makeig [smakeig at gmail.com]
> Sent: 07 October 2011 15:44
> To: Sara Graziadio
> Cc: David Groppe; japalmer29 at gmail.com; eeglablist at sccn.ucsd.edu
> Subject: Re: [Eeglablist] filters, ICA and erp
>
> Sara - I strongly believe in keeping as many degrees of freedom as possible when performing ICA. How many are possible?  This depends mainly on the length of the data. With 128 channels, ICA must learn a 128x128 unmixing matrix (or AMICA, the 128x128 mixing matrix plus other coefficients) -- to learn these 16k (or more) variables requires>>16k data points (perhaps 300,000 or more). At a 25- Hz sampling rate,  300k time points requires 20 min of data recording - usual EEG/ERP experiments are at least this long.
>
> Using PCA to reduce the dimension of the data unfortunately 'folds in' or 'squashes down' information in the smaller dimensions into the lower dimensional subspace -- I use the analogy of a 3-D data cloud shaped like a pizza with one small anchovy sticking up in the middle. After reducing the dimension of the data to 2 using PCA, the 'anchovy' portion of the data will be 'squashed down' into the (2-D) pizza (e.g., only its projection onto the pizza surface would be retained).  If the 'anchovy' dimension (i.e., that independent component and EEG source) were of interest, ICA could no longer find it. Even if not, it's presence in the (2-D) pizza would contribute noise to the (now 2-D) ICA decomposition...
>
> Scott
>
> On Fri, Oct 7, 2011 at 7:28 AM, Sara Graziadio<sara.graziadio at newcastle.ac.uk<mailto:sara.graziadio at newcastle.ac.uk>>  wrote:
> Do you mean that I should keep the external electrodes (that I was actually removing before the ica to have less noise) to have a better decomposition through  ica? I have 128 channels overall but I usually reduce the dimension to obtain around 20-30ICs (more than  97% of variance explained). The problem is though that the IC that have the muscle activity, when the muscle is silent, has cortical activity instead, if you know what I mean…don’t you have the same problem independently on the number of channels?
> Thanks
>
> Sara
>
> From: Scott Makeig [mailto:smakeig at gmail.com<mailto:smakeig at gmail.com>]
> Sent: 07 October 2011 15:20
> To: Sara Graziadio
> Cc: David Groppe; japalmer29 at gmail.com<mailto:japalmer29 at gmail.com>; eeglablist at sccn.ucsd.edu<mailto:eeglablist at sccn.ucsd.edu>
>
> Subject: Re: [Eeglablist] filters, ICA and erp
>
> 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<mailto: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<mailto:david.m.groppe at gmail.com>]
>> Sent: 05 October 2011 23:10
>> To: Sara Graziadio
>> Cc: eeglablist at sccn.ucsd.edu<mailto: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<mailto: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
>>> To unsubscribe, send an empty email to eeglablist-
>> unsubscribe at sccn.ucsd.edu<mailto:unsubscribe at sccn.ucsd.edu>
>>> For digest mode, send an email with the subject "set digest mime" to
>> eeglablist-request at sccn.ucsd.edu<mailto:eeglablist-request at sccn.ucsd.edu>
>>
>>
>> --
>> David Groppe, Ph.D.
>> Postdoctoral Researcher
>> North Shore LIJ Health System
>> New Hyde Park, New York
>> http://www.cogsci.ucsd.edu/~dgroppe/
> _______________________________________________
> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.ucsd.edu<mailto:eeglablist-unsubscribe at sccn.ucsd.edu>
> For digest mode, send an email with the subject "set digest mime" to eeglablist-request at sccn.ucsd.edu<mailto:eeglablist-request at sccn.ucsd.edu>
>
>
>
> --
> 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<http://sccn.ucsd.edu/%7Escott>
>
>
>
> --
> 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<http://sccn.ucsd.edu/%7Escott>
>
> _______________________________________________
> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.ucsd.edu
> For digest mode, send an email with the subject "set digest mime" to eeglablist-request at sccn.ucsd.edu

-- 
Prof. Dr. Stefan Debener
Neuropsychology	Lab
Department of Psychology
University of Oldenburg
D-26111 Oldenburg
Germany

Office: A7 0-038
Phone: +49-441-798-4271
Fax:   +49-441-798-5522
Email: stefan.debener at uni-oldenburg.de