MNE and EEG channel synchrony


I would like to use MNE in order to extract some measures of synchrony,
such as PLV, from EEG
samples. I saw the related connectivity module in MNE but I am unsure about
the input format
of the data for the spectral_connectivity function.

What I would like to do is to calculate the synchrony between channels that
are formatted as
a simple 2d numpy array where each row corresponds to a channel and each
column is a sample
so the format is (channel x time).
However the spectral_connectivity function expects data in the form:
array, shape=(n_epochs, n_signals, n_times) or generator of array, shape
=(n_signals, n_times).

So my questions are: Is there some way to use spectral_connectivity or some
other MNE module
to calculate the synchrony between EEG channels without epoch information?
Can I translate my 2d array into the array, shape =(n_signals, n_times)
format that MNE expects?

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Hi Theodore,

Most connectivity metrics are based on some sort of consistency of the
phase relationship between two signals across epochs/trials... In your
example, the PLV looks for how repeatable the phase difference between two
channels across epochs/trials...
  Thus you may want to consider in what sense you want to compute
synchrony and what that would mean before attempting to use the
spectral_connectivity(.) capabilities of MNE.