mne_connectivity.envelope_correlation¶
- mne_connectivity.envelope_correlation(data, names=None, orthogonalize='pairwise', log=False, absolute=True, verbose=None)[source]¶
Compute the envelope correlation.
- Parameters
data : array-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
SourceEstimateobject (andstc.datawill 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.
New in version 0.19.
log : bool
If True (default False), square and take the log before orthonalizing envelopes or computing correlations.
New in version 0.22.
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=True.New in version 0.22.
verbose : bool | str | int | 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.- Returns
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)
See also
Notes
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
combinefunction fromEpochConnectivityclasses.References
- 1
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.
- 2
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.
