Time delay estimator for EEG analysis based on information theory

The estimation of time delay between simultaneously recorded EEG (electroence-phalographic) signals is important for the determination of the area in the brain which is responsible for the synchronous activity seen during seizures in epilepsy patients. Methods based on the assumption of linear pathways of propagation in the brain -like the cross-correlation function- have been proposed with limited success. We propose a new analysis method -based on information theory- which does not require this assumption. In our approach, the Average Amount of Mutual Information (AAMI) is computed between pairs of signals, for a range of lag-values, analogous to the cross-correlation function. Estimation of AAMI requires the estimation of the simultaneous probability density function of the signal pairs. For this purpose, an iterative estimation procedure has been developed. Simulations on artificial signals, as well as applications to real EEG signals, show that this procedure is an improvement over the use of the cross-correlation function.