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the traditional way of implementing GC (i.e. comparing a full and a reduced model), leads to some issues, pointed out to in this paper https://www.pnas.org/content/114/34/E7063.short. The main issue is that the reduced model is VAR, the full model is VARMA; the full model has a given model order, the reduced model has order infinite by definition.
Luckily for the field, these issues had been already addressed prior to the Stokes&Purdon paper. One efficient way to solve them is by means of state space models.
More info (and pointers to code) here
Hello
the traditional way of implementing GC (i.e. comparing a full and a reduced model), leads to some issues, pointed out to in this paper https://www.pnas.org/content/114/34/E7063.short. The main issue is that the reduced model is VAR, the full model is VARMA; the full model has a given model order, the reduced model has order infinite by definition.
Luckily for the field, these issues had been already addressed prior to the Stokes&Purdon paper. One efficient way to solve them is by means of state space models.
More info (and pointers to code) here
https://f1000research.com/articles/6-1710
https://pubmed.ncbi.nlm.nih.gov/29883736/
I would be happy to help, I am not very familiar with the Brainstorm way of coding and dealing with data.
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