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.

covinstance of Covariance

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


The mne.Info object with information about the sensors and methods of measurement. Used specify the channels to include.

inverse_operatorinstance of InverseOperator

Inverse operator.


Number of averages used to regularize the solution.


The regularization parameter.

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

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

pick_oriNone | “normal”


  • 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.


If True, do not call prepare_inverse_operator().

labelLabel | None

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

method_paramsdict | None

Additional options for eLORETA. See Notes for details.


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

verbosebool | 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.

stcSourceEstimate | VectorSourceEstimate | VolSourceEstimate

The source estimates.

See also


Apply inverse operator to evoked object.


Apply inverse operator to raw object.


Apply inverse operator to epochs object.


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.



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.

Examples using mne.minimum_norm.apply_inverse_cov