Institute of Computer Science, Academy of Sciences of the Czech Republic
Pod vodárenskou vezí 2, 182 07 Prague 8, Czech Republic
An information-theoretic approach for studying synchronization phenomena in experimental bivariate time series is presented. ``Coarse-grained'' information rates are introduced and their ability to indicate generalized synchronization as well as to establish a ``direction of information flow'' between coupled systems, i.e., to discern the driving from the driven (response) system is demonstrated using numerically generated time series from unidirectionally coupled chaotic systems. The introduced method is then applied in a case study of EEG recordings of an epileptic patient. Synchronization events leading to seizures have been found on two levels of organization of brain tissues and ``directions of information flow'' among brain areas have been identified. The latter allows to localize the primary epileptogenic areas, also confirmed by the MRI and PET scans.