[Eeglablist] quick EEGLAB ICA question

[Jason Palmer]

Hi Jia,

There is some ambiguity in the case of non-stationary environments.
Generally for statistical estimation, more data means better estimates (of
components, activations, etc.), so you want as much data as possible.
However, if the data is non-stationary, then you have different data points
generated by different statistical systems, and combining the data will
generally degrade the estimation of either system. So you really want as
much data as possible generated from the statistical system of interest.

EEG data is usually preprocessed with a high-pass filter (remove mean and
low frequency drift) to increase the stationarity. We are essentially trying
to remove unimportant sources of non-stationarity to sample on from a
supposedly long-term stationary system of brain and other biological
sources.

Another issue is the number of sources that are present. Basic ICA assumes
that the number of sources is less than the number of sensors/channels. So
if you record from longer periods of time, you are likely to have more
"artifactual", transient type sources show up, which will force ICA to
compromise the independence of the estimated components.

If we assume that the same components are present at different times, and
that we have filtered and removed artifacts sufficiently to make the data
consist of fewer sources than sensors, then generally the more data the
better.

We might also assume that the same components are present in different
subjects. Again it will be important to try to preprocess out unimportant
differences (sources of nonstationarity) and try to be sure that the number
of sources is less than the number of data dimensions used.

Hope that's helpful.

Best,
Jason

 

---------- Forwarded message ----------
From: jia gu <jia.gu12345 at gmail.com>
Date: Mon, Jun 6, 2011 at 9:50 PM
Subject: quick EEGLAB ICA question
To: eeglab at sccn.ucsd.edu


To whom it might concern:

Thank you very much for providing us the EEGLAB!  :)
I am trying to use ICA to clean some EEG signals, I read that the min
# of sample points should be at least 25x channel number squared. But
there is no upper limit. I wonder does the performance of ICA
(infomax) get better with more training points? or does it start to
degrade after a certain optimal number of sample points, and if so
what is the best # of sample points?
thank you very much for your time and help
cheers
jia




-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation & Adj. Prof. of
Neurosciences, University of California San Diego, La Jolla CA 92093-0559,
http://sccn.ucsd.edu/~scott

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