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 (andstc.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
, orNone
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