Dear Alfredo,
Many thanks for your valuable points. regarding your questions:
-
Granger causality is a good approach to differentiate between the source and the sink of information flow. The performance of GC depends on how accurate is the underlying model and assumptions. Also, the interpretation is not very easy when we have more than two signals in our model. That is why we only have bi-variate functions. However, If you have few sources it should have satisfactory results.
-
We are working on documentation for the connectivity section and try to generate examples for sources as well. You can check the progress on the designated page. Hopefully, it will be available by the end of summer.
-
There are a couple of approaches for that. Sometimes you can find features from connectivity matrices (like network measures) and then compare those features between two groups. Another approach is to subtract one matrix from another one (if there are dependent groups like two conditions of a cognitive task) and compare the difference with a null hypothesis distribution to find out the connections with significant differences between groups.
Let me know if you need more information
- Hossein