mne.connectivity.envelope_correlation

mne.connectivity.envelope_correlation(data, combine='mean', verbose=None)[source]

Compute the envelope correlation.

Parameters
dataarray_like, shape=(n_epochs, n_signals, n_times) | generator

The data from which to compute connectivity. The array-like object can also be a list/generator of array, each with shape (n_signals, n_times), or a SourceEstimate object (and stc.data will be used). If it’s float data, the Hilbert transform will be applied; if it’s complex data, it’s assumed the Hilbert has already been applied.

combine‘mean’ | callable()None

How to combine correlation estimates across epochs. Default is ‘mean’. Can be None to return without combining. If callable, it must accept one positional input. For example:

combine = lambda data: np.median(data, axis=0)
verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns
corrndarray, shape ([n_epochs, ]n_nodes, n_nodes)

The pairwise orthogonal envelope correlations. This matrix is symmetric. If combine is None, the array with have three dimensions, the first of which is n_epochs.

Notes

This function computes the power envelope correlation between orthogonalized signals [1] [2].

References

1

Hipp JF, Hawellek DJ, Corbetta M, Siegel M, Engel AK (2012) Large-scale cortical correlation structure of spontaneous oscillatory activity. Nature Neuroscience 15:884–890

2

Khan S et al. (2018). Maturation trajectories of cortical resting-state networks depend on the mediating frequency band. Neuroimage 174:57–68