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.
- info
dict
The measurement info to specify the channels to include.
- inverse_operatorinstance of
InverseOperator
Inverse operator.
- nave
int
Number of averages used to regularize the solution.
- lambda2
float
The regularization parameter.
- method“MNE” | “dSPM” | “sLORETA” | “eLORETA”
Use minimum norm, dSPM (default), sLORETA, or eLORETA.
- pick_ori
None
| “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
Label
|None
Restricts the source estimates to a given label. If None, source estimates will be computed for the entire source space.
- method_params
dict
|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
, orNone
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.
- covinstance of
- Returns
- stc
SourceEstimate
|VectorSourceEstimate
|VolSourceEstimate
The source estimates.
- stc
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.