Multitaper Spectral Granger Causality with Application to Ssvep

The traditional parametric approach to Granger causality (GC), based on linear vector autoregressive modeling, suffers from difficulties related to the inaccurate modeling of the generative process. These limits can be solved by using nonparametric spectral estimates in the frequency-domain formulation of GC, also known as spectral GC. In a simulation study, we compare the mean square error of the estimated spectral GC using different multitaper spectral estimators, finding that the Peak Matched multitapers are preferable for estimating spectral GC characterized by peaks. As an illustrative example, we apply the non-parametric approach to the analysis of brain functional connectivity in steady-state visually evoked potentials.