[Eeglablist] ICA (number of data points) for artifact rejection

[Eric Landsness]

In previous eeglablist emails and the literature the number of datapoints needed for ICA decomposition has been discussed.  See 

http://sccn.ucsd.edu/pipermail/eeglablist/2008/002384.html
http://sccn.ucsd.edu/pipermail/eeglablist/2006/001568.html

My dataset is 8 min of continuous data with 256 channels at 500 Hz per subject which is way below the suggested sample size (256^2 * 20 = 44 minutes).

I am using ICA purely for artifact removal (eye and EMG removal) and was wondering what is the harm in having too little data?  How much risk do I run of removing "real" data? 

I understand that my data will be overlearned (Sarela and Vigario 2003), but I feel that the eye movements and EMG are being cleanly decomposed into a few components that when removed significantly improve my data set, is this a problem?  I am not using the decomposition as a training set for other data sets.  With each subject I run a new ICA decomposition.

Thanks for the comments and sorry to bring up this issue again to the email list.
Eric