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

This paper presents a neurophysiologically relevant model in which vectoria l epileptiform electroencephalographic (EEG) signals are produced from mult iple coupled neural populations. This model is used to evaluate the perform ances of non-linear regression analysis as a method to characterize couplin gs between neural populations from EEG signals they produce. Two quantities , estimated on generated signals, namely the non-linear correlation coeffic ient and the direction index, are related to the degree and direction of co upling parameters of the model. Their statistical behavior is first studied on a set of signals simulated for relevant configurations of the model. Th ey are then measured on real stereoelectroencephalographic (SEEG) signals. Results obtained in three patients suffering from temporal lobe epilepsy (T LE) show that abnormal functional couplings between cerebral structures, th at establish during seizures, can be interpreted in terms of causality. Per spectives are oriented to the identification of epileptogenic networks in T LE. (C) 2001 Elsevier Science Ireland Ltd. All rights reserved.