[FieldTrip] Granger Causality / MVAR modelling problem

[Velden, Daniel]

Dear community,

I am working on resting-state EEG data with around 40 trials per subject, each trial with a length of 10 seconds (sfrequency= 150Hz). This data is preprocessed using fieldtrip functions (Low-, Highpass filtering, ICA etc.) and then source reconstructed with the LCMV beamformer to 2338 source points on the individual subjects cortex.

Now I want to estimate Granger Causality (GC) for all of the 2338 source points, which requires mvar models of each subjects data. Here I use the "BioSig" toolkit with the default Vieira-Morf algorithm to remodel my data. While modelling most of my matrices are very badly conditioned with rcond's of e-18 and lower.

After modelling it is necessary to choose the best fitting model order. So I calculate the Akaike and Bayesian Information criterion (AIC, BIC). But the AIC and BIC results are all "Inf" or "-Inf", since the determinate of the noisecovariance results in that.

So does anyone else came across this issue? My presumption is, that the enormous amount of 2338 source/signals unavoidably results in badly conditioned matrices, since the number of comparisons/predictions that need to be made is too high and the algorithm assumes that every source/signal takes a role in every prediction of every value, therefore resulting in very small values for each ar-coefficient.

I appreciate any comment and/or contribution to that topic.

Greetings and all the best,
Daniel van de Velden

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Daniel van de Velden (M.Sc.) || PhD candidate
Wissenschaftlicher Mitarbeiter
Klinik für Klinische Neurophysiologie
Georg-August-Universität Göttingen
Robert-Koch.Str. 40, 37075 Göttingen
Tel. 0551- 39-65106

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https://doi.org/10.1371/journal.pcbi.1002202