hi,
These are good questions, for which there aren't clear answers. The structure of the data suggests using a hierarchical Bayesian model, where you would pool parameters over different data sets, but also allow for per data set parameter values. The pooled
parameters can place priors on the per day parameters etc. Usually there are several variations of the hierarchical structure and Bayesian model comparison can be used to choose the 'best' model.
If that's of interest, please read more in the Stan User Guide to get ideas on structuring your overall model for your dataset. TVB-style models can be implemented in Stan as part of your hierarchical model or in another probabilistic programming framework (e.g. TensorFlow probability).
cheers,
Marmaduke
hi
Yes best fits in two recordings reflect scan-rescan variability. I don't think attributing it to overfitting per se is correct though, as it could be the estimator (PSD with short time window is a high variance estimator, better to use a windowed method) or actual variability in the brain.
cheers,
Marmaduke