The method is based on the calculation of the probabilities p_X in the space of Fourier-coefficients. E.g., for class A log(p_A)=-2*tr(D*C_A^(-1))+constant where C_A is the averaged cross-spectrum at a specific frequency (here, alpha in extreme wide-band) and D is the cross-spectrum of the single trial. The feature space is one-dimensional: f(D)=log(p_A)-log(p_B) On the training set we found that the results improve if we heavily regularize the data by transforming them for after detrending as x(t)=a*tanh(x(t)/a) for each channel. We set a=alpha*std(x) where alpha is set to a very small number (.0001) such that the data are essentially reduced to the signs.