[Eeglablist] questions about N components and high pass filtering

Thu Apr 9 06:34:58 PDT 2009

Mike - I agree it is important to apply appropriate measures to quantify the
quality of an ICA decomposition. Jason Palmer has contributed a measure of
overall reduction in mutual information that could be used to quantify how
much data is 'enough' -- we intend to publish it soon. To determine the
acceptability of individual ICs, we find that scalp map dipolarity
(resemblance to the projection of a single equivalent dipole) is of
interest. But I am sure there is much more to be learned here - and very
likely much already known to applied mathematicians in the ICA and
information theory communities that could be useful ...

Scott Makeig

On Thu, Apr 9, 2009 at 6:15 AM, Michael Stevens <msteven at harthosp.org>wrote:

>  One thought about number of components might be to apply information
> theoretic tools to your data that essentially ask 'How much unique
> information is contained within my data?'.  Our colleagues have done this
> for ICA of fMRI data with some success... Although I've not yet
> systematically thought about its application to EEG data, on first blush it
> seems like a comparable approach.  For example, there are tools like Minimum
> Description Length (MDL) or AIC that might be useful.  Scott or others might
> have opinions about any pros/cons with using various algorithms with EEG
> data.
>
> It's also worth pointing out that any choice you make about how many
> components to estimate is, by definition, arbitrary.  When viewed that way,
> the issue becomes more about trusting what process or methods you used to
> make the most educated guess... and being able to defend the thought process
> and assumptions you made to get you to that number.
>
> Hope that's a little useful.
> Mike
>
>
>  Michael C. Stevens, Ph.D.
>
> Director, Child and Adolescent Research
> The Institute of Living / Hartford Hospital
>
> Director, Clinical Neuroscience & Development Laboratory
> Olin Neuropsychiatry Research Center
>
> Assistant Clinical Professor of Psychiatry
> Yale University School of Medicine
>
> Contact Information:
> 200 Retreat Avenue
> ONRC, Whitehall Building
> Hartford, CT  06106
>
> Tel: (860) 545-7552
> Fax: (860) 545-7797
> http://www.nrc-iol.org/onrc_labs_cnd.asp
>
>
> >>>
>   *From: *
> Scott Makeig <smakeig at gmail.com> *To:*
> Dorothy Bishop <Dorothy.Bishop at psy.ox.ac.uk> *Date: * 4/6/2009 5:04 PM *Subject:
> * Re: [Eeglablist] questions about N components and high pass filtering *
> CC:* <eeglablist at sccn.ucsd.edu>Dorothy -
>
> On Mon, Apr 6, 2009 at 8:28 AM, Dorothy Bishop <
> Dorothy.Bishop at psy.ox.ac.uk> wrote:
>
>> 1. If you are doing ICA with the view to removing noise components from a
>> signal, is there an optimal number of components to extract? The manual
>> gives guidance on how to compute the maximum number, but is it more
>> efficient to reduce the data to fewer dimensions? My impression is yes, but
>> I'd be grateful for the views of others, especially if there is some
>> rational means of deciding, rather than relying on trial and error.
>
>
> > For me, the key factor is how much data you have (timepoints /
> channels^2). If this is > 30 (or near to it), then we find it preferable to
> return all possible components (since pca does a rather poor job of
> separating sources). How many components to identify as 'noise' depends on
> your definition and interests. Simple PCAcompatible concepts such as EEG =
> signalspace + noisespace are not sufficient here, as ICA separates all sorts
> of "non-cortical brain EEG source processes" (aka noise) from each other.
>
>>
>> 2. It's not uncommon in my area for people to filter the data prior to
>> processing, and 1 Hz is a common value to select for high pass cutoff.
>> However, I'm concerned that if the SOA is around 1 second, then this filter
>> may remove genuine upward or downward trends in the data that are
>> stimulus-related. Have others got views and/or recommendations on this?
>
>
> > This is a difficult question. IF the sources of < 1 Hz data are spatially
> different from those at higher frequencies (e.g., from sweating, etc), then
> removing them (or decreasing them, actually) by frequency filtering may make
> sense (we routinely do it). However, if the low frequency activity is from
> discrete, spatially stationary sources (the same as the sources of
> higher-frequency EEG, or not), then leaving them in the data for ICA
> decomposition may well be preferable.
>
> Scott Makeig
>
>>
>> Many thanks.
>>
>> Dorothy Bishop
>> Professor of Developmental Neuropsychology
>> Department of Experimental Psychology
>> University of Oxford
>> OX1 3UD
>> http://psyweb.psy.ox.ac.uk/oscci/
>>
>> tel: +44 (0)1865 271369
>> fax: +44 (0)1865 281255
>>
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>
> --
> Scott Makeig, Research Scientist and Director, Swartz Center for
> Computational Neuroscience, Institute for Neural Computation, University of
> California San Diego, La Jolla CA 92093-0961, 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, University of
California San Diego, La Jolla CA 92093-0961, http://sccn.ucsd.edu/~scott
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