Klados - Good question - at this stage, this figure is totally empirical / heuristic. -Scott Makeig<br><br><div class="gmail_quote">On Wed, Apr 8, 2009 at 10:14 AM, Klados Manousos <span dir="ltr"><<a href="mailto:mklados@med.auth.gr">mklados@med.auth.gr</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">Dear EEGLAB users,<br><br>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? <br>
Thank you in advance<br><br><div class="gmail_quote">2009/4/7 Scott Makeig <span dir="ltr"><<a href="mailto:smakeig@gmail.com" target="_blank">smakeig@gmail.com</a>></span><div><div></div><div class="h5"><br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">Dorothy -<br><br><div class="gmail_quote"><div>On Mon, Apr 6, 2009 at 8:28 AM, Dorothy Bishop <span dir="ltr"><<a href="mailto:Dorothy.Bishop@psy.ox.ac.uk" target="_blank">Dorothy.Bishop@psy.ox.ac.uk</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
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.</blockquote>
</div><div><br>> 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.<br>
</div><div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"><br>
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?</blockquote>
</div><div><br>> 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.<br>
<br>Scott Makeig<br></div><div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"><br>
Many thanks.<br>
<br>
Dorothy Bishop<br>
Professor of Developmental Neuropsychology<br>
Department of Experimental Psychology<br>
University of Oxford<br>
OX1 3UD<br>
<a href="http://psyweb.psy.ox.ac.uk/oscci/" target="_blank">http://psyweb.psy.ox.ac.uk/oscci/</a><br>
<br>
tel: +44 (0)1865 271369<br>
fax: +44 (0)1865 281255<br>
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</blockquote></div></div><font color="#888888"><br>-- <br>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, <a href="http://sccn.ucsd.edu/%7Escott" target="_blank">http://sccn.ucsd.edu/~scott</a><br>
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<br clear="all"><br>-- <br>Klados A. Manousos<br>
Graduate Student, Research Assistant<br>Group of Applied Neurosciences<br>Lab of Medical Informatics, Medical School<br>Aristotle University of Thessaloniki<br>Thessaloniki, Greece<br>_________________________________________________<br>
Tel: +30-2310-999332<br>Website :<br><a href="http://lomiweb.med.auth.gr/gan/mklados/" target="_blank">http://lomiweb.med.auth.gr/gan/mklados/</a><br><br><br>
</blockquote></div><br><br clear="all"><br>-- <br>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, <a href="http://sccn.ucsd.edu/~scott">http://sccn.ucsd.edu/~scott</a><br>