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:

dataarray_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 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.
nameslist | 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.
logbool: If True (default False), square and take the log before orthogonalizing envelopes or computing correlations.
absolutebool: 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'.
verbosebool | 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.

Returns:

corrinstance of EpochTemporalConnectivity: 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).

Examples using `mne_connectivity.envelope_correlation`#