[Eeglablist] questions about N components and high pass filtering

[Scott Makeig]

Klados - Good question - at this stage, this figure is totally empirical /
heuristic.   -Scott Makeig

On Wed, Apr 8, 2009 at 10:14 AM, Klados Manousos <mklados at med.auth.gr>wrote:

> Dear EEGLAB users,
>
> I have a question about the formula Dr. Makeig mentioned in his answer to
> Dr's Bishop question. Dr Makeig mentiones that "(timepoints / channels^2).
> If this is > 30 (or near to it), then we find it preferable to return all
> possible components" How does this formula had been educed? Is there a
> published work, which prooves its correctness? Or it is totaly empirical?
> Thank you in advance
>
> 2009/4/7 Scott Makeig <smakeig at gmail.com>
>
> 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
>>>
>>> _______________________________________________
>>> 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
>>>
>>
>> --
>> 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>
>>
>> _______________________________________________
>> 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
>>
>
>
>
> --
> Klados A. Manousos
> Graduate Student, Research Assistant
> Group of Applied Neurosciences
> Lab of Medical Informatics, Medical School
> Aristotle University of Thessaloniki
> Thessaloniki, Greece
> _________________________________________________
> Tel: +30-2310-999332
> Website :
> http://lomiweb.med.auth.gr/gan/mklados/
>
>
>


-- 
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
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20090408/f44fa740/attachment.html>