[Eeglablist] Pipeline of processing to optimize ICA for artrifact removal

[Radu Ranta]

Hi all,

A small comment on the second remark of Arno - in our experience, it is
useful to use the average reference and to include the average as a
supplementary channel when doing ICA. Averaging diminishes the additive
noise, while keeping the average as a signal preserves the rank of your
data thus the number of recovered sources. See Salido-Ruiz et al, in
Biomedical Signal Processing and Control, January 2011.

We have asked ourselves the pipeline question, although in a different
context (artefact elimination combined to wavelet denoising). We have
found that aggressive pre-processing tend to worsen the quality of the
ICA decomposition (useful components for the ICA might be eliminated
from the channels where they present but below some threshold). Our
pipeline proposal is available on-line on the Biomedical Signal
Processing and Control site (Romo-Vazquez et al, not yet printed in
paper version).

Best, 
Radu

Le jeudi 23 février 2012 à 09:04 -0800, Arnaud Delorme a écrit :
> Dear Mosdestino,
> 
> 
> 1. Difficulties with ICA. When removing ICA components, one of the
> main concern is the quality of your decomposition. We are currently
> working on tools to assess this quality although this can be tricky
> because of the large inter-subject variability. In the meantime, if
> you have multiple components for each type of artifacts, this is
> usually a sign that the quality of your decomposition is poor. One of
> the main factor to increase quality is to increase the amount of data
> and also high pass filtering if you have large offset in your data
> channels.
> 
> 
> 2. The reference should not alter the decomposition because it is a
> linear transformation. This said, it does in some cases, because of
> numerical implementation of ICA algorithms. I have found myself that
> average reference tend to perform better than single reference.
> However, this is a qualitative judgment and I could be proven wrong in
> the future. In general, ICA works irrespective of the reference you
> use.
> 
> 
> 3. Yes, you should include the EOG channels if you have recorded them
> using the same reference as the other channels. Otherwise you should
> exclude them.
> 
> 
> 4. It is true that high pass filtering above 0.5 Hz tends to remove
> slow drifts. ICA is sensitive to these drifts. Some users even like to
> filter above 2 Hz for noisy datasets, but then you might loose
> interesting data. However, again there has not been a clear
> demonstration that the quality of ICA increases with filtering. When
> the analog filter of the amplifier is sufficient, and data channels do
> not exhibit strong offsets, ICA performs well on raw data. Note that
> you should not apply a decomposition obtained on filtered data on an
> unfiltered dataset (the large offset in your data channels which have
> been removed by filtering will be used to compute ICA component
> activity -- your ICA component activities will be ruined).
> 
> 
> 5. You can detrend if you want. It should not affect much the data for
> long records. Most likely, the ICA decomposition will remain
> unchanged.
> 
> 
> 6. ICA signal amplitude is -- unless you unselect the option -- scaled
> to microvolt. The default value for thresholding might not be optimal
> so adjust it manually.
> 
> 
> Best,
> 
> 
> Arno
> 
> On Feb 21, 2012, at 6:20 AM, Modestino, Edward J *HS wrote:
> 
> > Hello Experts,
> > I am trying to come up with a pipeline of my own processing
> > including using ICA to remove artifacts.  I am hoping people will be
> > generous enough to share with me their knowledge on this topic. 
> >  
> > First, in using ICA, I have found many difficulties.  I have
> > attempted to remove artifacts like lateral eye movements and eye
> > blinks using ICA.  In some cases it has appears that certain
> > components which match topographically the known presentation of eye
> > movements, when removed, appear to be taking some of the cortical
> > EEG with it.  This is consistent with Lindsen and Bhattacharya
> > (2010).  In other cases, it appears that residue of the artifact
> > still remains in the EEG after component removal.  This is
> > consistent with Shackman, McMenamin, Maxwell, Greischar and Davidson
> > (2010) and at least two other people reporting this to the email
> > listserv only within the last week.  I have used various EEGLAB
> > plugins to choose which components should be considered artifacts
> > based on statistics and still found some components that were
> > clearly artifacts not earmarked for removal as artifacts.   
> >  
> > Second, I am working with Biosemi and another system with data that
> > is unreferenced.  I am wondering if it would be easier to perform
> > ICA on unreferenced data to avoid mixing the sources even further
> > with a common average reference. 
> >  
> > Third, should I include bipolar EOG channels in ICA.  It does not
> > seem like a good idea as they are referenced differently than the
> > EEG channels. 
> >  
> > Fourth, I know that Arno has mentioned that ICA performs better on
> > data that has been high-pass filtered at 0.5 or even1 Hz to remove
> > skin conductance and other slow signals that are not of cortical
> > origin.  Some others have suggested doing ICA on data filtered this
> > way and then applying the ICA weighting to the unfiltered raw data.
> > I am not sure how this would work and if it can be justified. 
> >  
> > Fifth, should I detrend the data (remove the mean) from the
> > continuous data?  Or should I detrend at the epoch level or merely
> > baseline correct (remove the baseline mean from only the baseline
> > period)?  If I am doing spectral analysis, I believe detrending is
> > necessary.  In such a case, should this be done on the continuous
> > data or on the epoched data?
> >  
> > Sixth, I feel confident using a thresholding technique to remove
> > excursions greater than +/-60 microvolts as shown by Fu et al.
> > (2001) as artifacts.  I can set this up in the automated artifact
> > removal in EEGLAB.  If I were to use  the default options on the
> > thresholding or any other statistical means of classifying
> > artifacts, often all of my data is gone.  I thought using the
> > automated and default settings might be good to preprocess the data
> > BEFORE using ICA decomposition.  However, as there is often no data
> > left, or very little, perhaps 20-40% of the epoched data if I am
> > lucky, if I am unlucky none, this has not worked out well.
> >  
> > I have been trying various combinations of all of these and still
> > feel like I need some input from others as to the order of these
> > things (it may not matter if these are linear processes). 
> >  
> > My feeling is all (or some) of these things may have an effect on
> > the outcome of ICA decomposition and subsequently artifact removal.
> > As I am finding residual artifacts left after component removal and
> > sometimes even what appears to be EEG cortical signal removed, I
> > thought it might be a good idea to get input on these things for an
> > optimal pipeline.  Please let me know your thoughts on a pipepline
> > you prefer, what steps I should use and perhaps even an order of
> > steps. 
> >  
> > Thanks in advance for your help!
> > Dr. Modestino
> >  
> > Edward Justin Modestino, Ph.D.
> > Postdoctoral Research Associate
> > Ray Westphal Neuroimaging Laboratory
> > Division of Perceptual Studies
> > Department of Psychiatry and Neurobehavioral Sciences
> > University of Virginia
> > Email: ejm9f at virginia.edu
> >  
> >  
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-- 
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E-mail : Radu.Ranta at ensem.inpl-nancy.fr

Radu RANTA
Nancy Université/INPL - ENSEM - CRAN
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