Adaptive kernels and transfer entropy for neural connectivity analysis in EEG signals

This paper aims at better understanding causal relationships between parts of the brain during epileptic seizures. Our objective is to detect effective connectivity, i.e. to discover whether neural activity in a given substructure influences activity in another part of the brain. Recent efforts have been devoted to develop nonlinear and nonparametric approaches, such as transfer entropy (TE), to overcome linear methods limitations. However, building efficient TE estimators still asks open questions. In this study, we propose a new strategy to improve TE estimation by introducing different nonparametric adaptive kernel density estimators. Among all techniques under study, the Gaussian adaptive kernel density estimator based approach presents the best behavior whatever the tested model (autoregressive or physiological model).