Interpretation of interdependencies in epileptic signals using a macroscopic physiological model of the EEG

This paper presents a neurophysiologically relevant model in which vectorial epileptiform electroencephalographic (EEG) signals are produced from multiple coupled neural populations. This model is used to evaluate the performances of non-linear regression analysis as a method to characterize couplings between neural populations from EEG signals they produce. Two quantities, estimated on generated signals, namely the non-linear correlation coefficient and the direction index, are related to the degree and direction of coupling parameters of the model. Their statistical behavior is first studied on a set of signals simulated for relevant configurations of the model. They are then measured on real stereoelectroencephalographic (SEEG) signals. Results obtained in three patients suffering from temporal lobe epilepsy (TLE) show that abnormal functional couplings between cerebral structures, that establish during seizures, can be interpreted in terms of causality. Perspectives are oriented to the identification of epileptogenic networks in TLE.