mne_connectivity.envelope_correlation(data, names=None, orthogonalize='pairwise', log=False, absolute=True, verbose=None)[source]#

Compute the envelope correlation.


data : array_like, shape=(n_epochs, n_signals, n_times) | Epochs | 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.

names : list | array_like | None

A list of names associated with the signals in data. If None, will be a list of indices of the number of nodes.

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.

log : bool

If True (default False), square and take the log before orthonalizing envelopes or computing correlations.

absolute : bool

If True (default), then take the absolute value of correlation coefficients before making each epoch’s correlation matrix symmetric (and thus before combining matrices across epochs). Only used when orthogonalize='pairwise'.

verbose : bool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.


corr : instance of EpochConnectivity

The pairwise orthogonal envelope correlations. This matrix is symmetric. The array will have three dimensions, the first of which is n_epochs. The data shape would be (n_epochs, (n_nodes + 1) * n_nodes / 2).


This function computes the power envelope correlation between orthogonalized signals 12.

If you would like to combine Epochs after the fact using some function over the Epochs axis, see the combine function from EpochConnectivity classes.



Joerg F Hipp, David J Hawellek, Maurizio Corbetta, Markus Siegel, and Andreas K Engel. Large-scale cortical correlation structure of spontaneous oscillatory activity. Nature Neuroscience, 15(6):884–890, 2012. doi:10.1038/nn.3101.


Sheraz Khan, Javeria A. Hashmi, Fahimeh Mamashli, Konstantinos Michmizos, Manfred G. Kitzbichler, Hari Bharadwaj, Yousra Bekhti, Santosh Ganesan, Keri-Lee A. Garel, Susan Whitfield-Gabrieli, Randy L. Gollub, Jian Kong, Lucia M. Vaina, Kunjan D. Rana, Steven M. Stufflebeam, Matti S. Hämäläinen, and Tal Kenet. Maturation trajectories of cortical resting-state networks depend on the mediating frequency band. NeuroImage, 174:57–68, 2018. doi:10.1016/j.neuroimage.2018.02.018.

Examples using mne_connectivity.envelope_correlation#