Statistical assessment of nonlinear causality: application to epileptic EEG signals.

In this study an information-theoretic test for general Granger causality is used to identify couplings and information transport between different brain areas during epileptic activities. This method can distinguish information that is actually exchanged between two systems from that due to the response to a common signal or past history. This is achieved by an appropriate conditioning of probabilities. Statistical assessment of causality is made from a nonparametric bootstrap test, whereas nonlinearity is assessed by a comparison with a linearized version of the causality index. The framework proposed here provides a useful and model free test to characterize interactions in intracranial electroencephalography (EEG) signals.