[Eeglablist] questions about N components and high pass filtering

[Klados Manousos]

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
>>
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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>
>
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-- 
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/
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