[Eeglablist] questions about N components and high pass filtering

[Dorothy Bishop]

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.

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?

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
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