In previous publications,^1-6 several approaches targeting the problem of seizure prediction⁷ in epilepsy⁸ have been proposed. In this contribution recent results based on an EEG-signal prediction algorithm will be presented and discussed in detail. Therefore segmented data aquired by multi-electrode Stereoelectroencephalography (SEEG) and Electrocorticography (ECoG) are presented to a delay-type DTCNN with linear weight functions and a 3 × 1 network topology. This leads to series of signal predictors and according to that to series of prediction errors. These prediction error series are arranged in a 2 dimensional representation called error profile.⁹ This profile enables the choice of optimal positions for implanting long time electrodes, by means of which perhaps a mostly effective seizure prediction may become possible. So far data of different patients have been studied in detail and some distinct electrode points were found showing distinct changes before a seizure onset.