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

[Modestino, Edward J *HS]

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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