We have shown in different studies (Gollas et al., 2004; Niederhofer and Tetzlaff, 2005; Weib and Tetzlaff, 2002) that the analysis of EEG-signals in epilepsy (Engels, 1989) using algorithms based on cellular nonlinear networks (CNN) (Leon and Chua, 1998) can contribute to the unsolved seizure prediction problem. For an automated prediction of impending epileptic seizures a precursor detection has to be performed which is based on an extraction of signal features in an pre-processing step. In different approaches (Fischer and Tetzlaff; Niederhofer et al., 2003, 2002; Kunz et al., 2000) to the feature extraction problem, weakly nonlinear discrete-time (DT) CNN with polynomial weight functions have been used especially for the signal prediction. In this paper the signal prediction by DT-CNN will be treated for increasing order of the polynomial weight functions. The aim of our work is to find out whether an increasing nonlinear degree will lead to more accurate results. Thereby the effects of taking EEG data as network boundary conditions will be studied