Pat suggested looking at logistic regression as a natural step from correlation coefficient. It seems quite complicated though.
Now that there is something to write about, there are some additional things to try.
For brain wave classification, which is a better measure of closeness, least-square or correlation coefficient? Today's computation gives some evidence that correlation coefficient might be a more effective measure. I took data from S6 and S7 from an earlier 60-channel EEG experiment and ran a 3 bin classification on monopolar and derived bipolar. Stimuli consisted of 7 words, first, second, third, yes, no, right and left, and were either auditory (S6A, S7A) or visual (S6V, S7V). In every case of the derived bipolar, use of the correlation coefficient increased the recognition rate by at least 0.7σ, in one case up to 1.8σ. Dfferences in rates for the monopolar montage were less conclusive, though, for every data set, classification on the derived bipolar using correlation coefficient performed the best. More details here.
With some help from Brad Lauster, I was finally able to get Moveable Type working on my Leland account. I've been meaning to write software like this for years - so it is nice that it is available for free. There were some issues though...
I had problems after moving the docs and images directory to my WWW directory. The problem was that I unzipped and installed the files into my cgi-bin directory then moved the docs and images directory. Somehow the permissions were all messed up and I was unable to look at files in those directories from the web. I ended up having to make new directories. Then I copied the files into those directories. That worked!