# mne.minimum_norm.apply_inverse_cov¶

mne.minimum_norm.apply_inverse_cov(cov, info, inverse_operator, nave=1, lambda2=0.1111111111111111, method='dSPM', pick_ori=None, prepared=False, label=None, method_params=None, use_cps=True, verbose=None)[source]

Apply inverse operator to covariance data.

Parameters
covinstance of Covariance

Covariance data, computed on the time segment for which to compute source power.

infodict

The measurement info to specify the channels to include.

inverse_operatorinstance of InverseOperator

Inverse operator.

naveint

Number of averages used to regularize the solution.

lambda2float

The regularization parameter.

method“MNE” | “dSPM” | “sLORETA” | “eLORETA”

Use minimum norm, dSPM (default), sLORETA, or eLORETA.

pick_oriNone | “normal”

Options:

• None

Pooling is performed by taking the norm of loose/free orientations. In case of a fixed source space no norm is computed leading to signed source activity.

• "normal"

Only the normal to the cortical surface is kept. This is only implemented when working with loose orientations.

preparedbool

If True, do not call prepare_inverse_operator().

label

Restricts the source estimates to a given label. If None, source estimates will be computed for the entire source space.

method_params

Additional options for eLORETA. See Notes for details.

use_cpsbool

Whether to use cortical patch statistics to define normal orientations for surfaces (default True).

verbose

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
stc

The source estimates.

apply_inverse

Apply inverse operator to evoked object.

apply_inverse_raw

Apply inverse operator to raw object.

apply_inverse_epochs

Apply inverse operator to epochs object.

Notes

New in version 0.20.

This code is based on the original research code from 1 and has been useful to correct for individual field spread using source localization in the context of predictive modeling.

References

1

David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, and Denis A. Engemann. Predictive regression modeling with meg/eeg: from source power to signals and cognitive states. NeuroImage, 2020. doi:10.1016/j.neuroimage.2020.116893.