Jeremy Hill (Max Planck Institute for Biological Cybernetics, Tuebingen) and 
Michael Schroeder (Computer Science department, Tuebingen University).

We interpolated each nan-sample linearly, then downsampled the data to 
100 Hz with a smooth falloff from 45 to 50 Hz, then did linear 
detrending, then performed infomax ICA, then obtained amplitude spectra 
using Welch's method with 5 overlapping hanning-windowed segments, then 
performed linear PCA on all the resulting features.  We then split the 
problem up into binary contrasts and trained linear SVMs. For each 
binary classification, we performed 10-fold CV to find the best 
regularizer and the optimal number of PCs (PCs were ordered by a 
ROC-based method).  We performed all pairwise contrasts, all 
one-against-rests, and all contrasts between merged pairs of classes, 
for a total of 13 output values. These were then decoded to 4 class 
scores using loss-based decoding. The scores are constant for the 
entire duration of each trial.