Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals

In the field of epilepsy, the analysis of stereoelectroencephalographic (SE EG, intra-cerebral recording) signals with signal processing methods can he lp to better identify the epileptogenic zone, the area of the brain respons ible for triggering seizures, and to better understand its organization. In order to evaluate these methods and to physiologically interpret the resul ts they provide, we developed a model able to produce EEG signals from "org anized" networks of neural populations. Starting from a neurophysiologicall y relevant model initially proposed by Lopes Da Silva et al. [Lopes da Silv a FH, Hoek A, Smith H, Zetterberg LH (1974) Kybernetic 15: 27-37] and recen tly re-designed by Jansen et al. [Jansen BH, Zouridakis G, Brandt ME (1993) Biol Cybern 68: 275-283] the present study demonstrates that this model ca n be extended to generate spontaneous EEG signals from multiple coupled neu ral populations. Model parameters related to excitation, inhibition and cou pling are then altered to produce epileptiform EEG signals. Results show th at the qualitative behavior of the model is realistic; simulated signals re semble those recorded from different brain structures for both interictal a nd ictal activities. Possible exploitation of simulations in signal process ing is illustrated through one example; statistical couplings between both simulated signals and real SEEG signals are estimated using nonlinear regre ssion. Results are compared and show that, through the model, real SEEG sig nals can be interpreted with the aid of signal processing methods.