[Mne_analysis] Are TF-MxNE timecourses appropriate for Granger causality?

[Per Arnold Lysne]

Hello,



    I am looking for a sparse MEG inverse solution that would be appropriate for input to Granger causality analysis. In particular, since Granger causality is usually implemented by linear means, would the output from a non-linear, sparse inverse solution such as TF-MxNE be appropriate here? I have not been able to determine this from Gramfort's 2013 NeuroImage paper or other sources (probably  because of my own mathematical shortcomings). In particular, I cannot tell if the non-linearity in TF-MxNE is limited to the localizations (which would be acceptable) or if it applies to the corresponding timecourses as well (in which case I would expect it to disrupt linear Granger analysis).



    I am using the non-parametric Granger causality methods of Dhamala, Rangarajan, and Ding (Physical Review Letters, NeuroImage, 2008, where Wilson's 1972 numerical spectral decomposition is used in place of MVAR estimation), and the ability of TF-MxNE to work with non-stationary data is very appealing.



    Thanks again,



Per Lysne, The University of New Mexico
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