Howto calculate SNR for EEG data? This question is about SNR in the context of EEG. (a related question  about SNR)

I am interested in calculating The SNR of an ERP. My motivation is: To calculate the "signal-to-noise ratio" and from this to extract the information (how?) on how many trials are needed to construct a stable ERP.

my Main question is: how do I calculate and SNR for such scenario  ?

some more explation about EEG and ERP are enclosed:
EEG:


  The on-going electrical activity of the brain measured from scalp electrodes is called the electroencephalogram or EEG.  within the EEG is a signal which is more revealing about information processing in the brain. This signal can be obtained by time-locking the recording of the EEG to the onset of events such as a person reading a word on a computer screen, listening to a musical note played on an instrument, or viewing a picture in a magazine. The resulting activity is called an "event-related potential" (ERP)


SNR:


  The number of trials necessary to obtain an ERP depends on a number of factors, the most important being the "signal-to-noise ratio", that is, the relative size of the signal (the ERP) relative to the size of the noise (the background EEG). In cognitive experiments 30 to 50 stimulus presentations are typically required to obtain a good clean average ERP.


from: http://neurocog.psy.tufts.edu/images/erp.htm
First you have to clearly define what you mean by "noise". Is simply the electronic noise in your measurement system or is it also brain activity that's not related to your stimulus. 

One easy way to determine SNR is to compare the signal level during a stimulus with the signal level without stimulus. If noise and signal are uncorrelated you can determine the signal level through energy subtraction and thus determine the SNR.  

Depending on your noise definition, this may not be practical cause you can't easily shut off the brain of the subject :-).