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

Thu Feb 23 20:37:46 PST 2012

Another comment on (5) below:  David Groppe pointed out clearly that
applying baseline correction to individual data epochs before ICA is a poor
idea, since the scalps of those corrections to each channel for each epoch
are a kind of noise you are adding to the data.  Remove channel means for
the continuous data ( after rejection of unusally large / 'bad' data
stretches).  -Scott Makeig

On Thu, Feb 23, 2012 at 8:33 PM, Scott Makeig <smakeig at gmail.com> wrote:

> Some additional >> comments,   Scott Makeig
>
> On Thu, Feb 23, 2012 at 9:04 AM, Arnaud Delorme <arno at ucsd.edu> wrote:
>
>> 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.
>>
>> >> Modestino, you do not mention the data length he is using. An
> important value is #timepoints/(#channels)^2 ... this should be much more
> than 1 (even as high as 30 for 256 channels, in our (limited) experience --
> I hope we will run a numerical experiment on this soon...  Running ICA on
> insufficient length data is the most common problem in applying ICA
> successfully.
>
>
>> 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.
>>
>
> >> See my more extensive comment to this list a few days back...
>
>
>> 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).
>>
>> >> I think Arno's 'ruined' above is a bit strong. If you have not trained
> ICA on some portion of the data, then applying an ICA unmixing matrix
> trained on some *other* data obviously may not give meaningful results --
> *unless* the new data has the *same* spatial source structure. In this
> case, does it??
>
>
>> 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.
>>
>
> >> Here I disagree -- large, long term offsets and linear trends will
> certainly affect the ICA decomposition (hence the practice of high pass
> filtering the data before ICA decomposition).
>
> 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.
>>
>
> >> To be more clear:  ICA decomposes the data into a product of fixed
> component scalp maps by maximally independent component time courses. When
> the scalp maps are normalized to RMS=1 amplitude (as generally in EEGLAB),
> then the RMS of the component time series must be the RMS contribution
> (across all the scalp channels) to the channel data.   -Scott Makeig
>
> 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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>
>
>
> --
> 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
>

-- 
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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