A non-parametric efficient evaluation of Partial Directed Coherence

Studying the flow of information between different areas of the brain can be performed by using the so-called Partial Directed Coherence. This measure is usually evaluated by first identifying a multivariate autoregressive model, and then by using Fourier transforms of the impulse responses identified and applying appropriate normalizations. Here, we present another route to evaluate the partial directed coherences in multivariate time series. The method proposed is non parametric, and utilises the strong spectral factorization of the inverse of the spectral density matrix of the multivariate process. To perform the factorization, we have recourse to an algorithm developed by Davis and his collaborators. We present simulations as well as an application on a real data set (Local Field Potentials in the sleeping mouse) to illustrate the methodology. A comparison to the usual approach in term of complexity is detailed. For long AR models, the proposed approach is of interest.

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