Hello there, 
I am working on a project where I aim to identify resting state networks using EEG data; and I could really use some guidance. I am new to EEG analysis, and while I have explored various resources; I still have a few questions that I am hoping this community can help with.
Collected EEG data from 15 participants during a 10-minute eyes-closed resting condition. Preprocessed the data using MNE-Python; including filtering; artifact rejection; and referencing to the average.
Applied FastICA to extract components that might represent RSNs.
How do I accurately identify which ICA components correspond to well known RSNs like the Default Mode Network? Are there specific spatial or spectral signatures I should look for? 
Once I think I have identified RSNs, what are the best practices for validating that these components are indeed RSNs? Are there any reliable metrics or visualization techniques that I should use?
Would using other approaches like microstate analysis or graph theory provide complementary insights? If so; how can I integrate these methods with my existing ICA results?
Also, I have gone through this post; https://neurostars.org/t/how-to-find-my-way-in-resting-state-fmri-methods-blue-prism which definitely helped me out a lot.
Thanks in advance for your help and assistance. 