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

Compute the envelope correlation.

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 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)
orthogonalize‘pairwise’ | False

Whether to orthogonalize with the pairwise method or not. Defaults to ‘pairwise’. Note that when False, the correlation matrix will not be returned with absolute values.

New in version 0.19.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

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.


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



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


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