Comparison of granger causality measures to detect effective connectivity in the context of epilepsy

Effective connectivity can be modeled and quantified with a number of techniques. The aim of this study is to reveal the direction of the information flow and to quantify the magnitude of coupling between epileptic brain structures using Granger Causality (GC) approaches. Since traditional linear GC cannot identify non-linear effects in the data, the non-linear extension of this measure is recommended. A comparative study between linear and non-linear GC is performed to determine the importance of the non-linear measure in the study of complex dynamical systems as neural networks. Experiments are first conducted on a linear autoregressive model, then on a non-linear model and finally on a model of intracranial EEG signals generation before giving some conclusions on the relevance on the different indices.