[Eeglablist] questions about N components and high pass filtering

[Scott Makeig]

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
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