EEG (Electroencephalography) is used to measure the electrical activity of a human brain. It is widely used to analyse both normal and pathological data, because of its very high temporal resolution. Different algorithms were proposed in the literature for EEG signal processing, but a difficult issue is their validation on real signals. An important goal is thus to realistically simulate EEG data. The starting point of this research was the model proposed by Rankine et al. for the surface newborn EEG signal generation. The model, based on both statistical, fractal and classical frequency modelling, has parameters estimated from the real data. A first objective is to validate and parametrize this model on adult surface EEG. A second and more important goal is to parametrize it and to apply it to depth EEG measurements (SEEG). The first results presented in this communication show that the proposed model can be applied in both cases (surface and depth adult EEG), although the parameters are slightly different. As expected, seizures cannot be modelled using this approach.