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.

infomne.Info

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.

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().

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.

use_cpsbool

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.

Returns
stcSourceEstimate | VectorSourceEstimate | VolSourceEstimate

The source estimates.

See also

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.