  In this paper, we developed a model-based and a data-driven estimator for
directed information (DI) to infer the causal connectivity graph between
electrocorticographic (ECoG) signals recorded from brain and to identify the
seizure onset zone (SOZ) in epileptic patients. Directed information, an
information theoretic quantity, is a general metric to infer causal
connectivity between time-series and is not restricted to a particular class of
models unlike the popular metrics based on Granger causality or transfer
entropy. The proposed estimators are shown to be almost surely convergent.
Causal connectivity between ECoG electrodes in five epileptic patients is
inferred using the proposed DI estimators, after validating their performance
on simulated data. We then proposed a model-based and a data-driven SOZ
identification algorithm to identify SOZ from the causal connectivity inferred
using model-based and data-driven DI estimators respectively. The data-driven
SOZ identification outperforms the model-based SOZ identification algorithm
when benchmarked against visual analysis by neurologist, the current clinical
gold standard. The causal connectivity analysis presented here is the first
step towards developing novel non-surgical treatments for epilepsy.
