  We propose a formal expansion of the transfer entropy to put in evidence
irreducible sets of variables which provide information for the future state of
each assigned target. Multiplets characterized by a large contribution to the
expansion are associated to informational circuits present in the system, with
an informational character which can be associated to the sign of the
contribution. For the sake of computational complexity, we adopt the assumption
of Gaussianity and use the corresponding exact formula for the conditional
mutual information. We report the application of the proposed methodology on
two EEG data sets.
