Hey Denis!
Thanks for the answer!
My problem isn't creating the events (actually I didn't know about this function and now I can delete the code I wrote for that), but how to send one window to the mne.connectivity.spectral_connectivity (one of the PLI methods) without getting all zeros or nans.
Thanks,
Noam
Ah no the assumption here is that you aggregate over trials. If you just
send one window you cannot get a meaningful score.
Yep, this is my problem 
Do you know any other approach for resting state connectivity?
Thanks,
Noam
Hi Noam,
Did you try envelope correlations or its flavors like orthogonal
correlations (Hipp or Brookes) ?
They are found to be most reliable for resting state connectivity.
http://www.sciencedirect.com/science/article/pii/S1053811916301914
HTH
Sheraz
Noam,
can you tell me more about your use case? Why would you want to compute
connectivity for one window only? If you have some real-time application
you would need to create "mini-epochs" of a few samples and then aggregate
over them using the epochs-oriented API, but you would run into estimator
problems as all these measures scale get more stable with more trials.
With the existing API, single-trial connectivity does not seem easy to
realise.
Sheraz,
I have a power envelopes implementation on one of my MNE-Python branches
and plan to commit it soon after some final validation is done. Pinging me
on this may speed up things. But also here you would go across trials.
Avniel, yes that's a nice paper, they managed to put together some nice
materials and making explicit intuitions that had been circulating for
quite some time without tapping into the absolute CFC bashing game; indeed
they are rather constructive. I also liked their idea that the notion of
spurious CFC maybe spurious itself. If CFC is due to harmonics it would at
least suggest non-sinusoidal waveforms. Curious to see where this goes.
Denis
I'm surprised nobody has suggested multi-tapering the epoch as chronux
does.
CWT should work similarly.
Hey Denis,
I want to calculate resting state connectivity across time. I'm trying to replicate Linda Douw<https://scholar.google.com/citations?hl=en&user=1YO2IvcAAAAJ&view_op=list_works&sortby=pubdate>'s work, where she correlated between cognitive flexibility and the coefficient of variation (Cv) of the functional connectivity. To calculate the Cv, I need first to calculate the functional connectivity using sliding windows, and assign a value for each window.
Thanks,
Noam