Apply inverse operator to covariance data.
Covariance
Covariance data, computed on the time segment for which to compute source power.
mne.Info
The mne.Info
object with information about the sensors and methods of measurement. Used specify the channels to include.
InverseOperator
Inverse operator.
int
Number of averages used to regularize the solution.
float
The regularization parameter.
Use minimum norm, dSPM (default), sLORETA, or eLORETA.
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.
If True, do not call prepare_inverse_operator()
.
Label
| None
Restricts the source estimates to a given label. If None, source estimates will be computed for the entire source space.
dict
| None
Additional options for eLORETA. See Notes for details.
Whether to use cortical patch statistics to define normal orientations for surfaces (default True).
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.
SourceEstimate
| 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
mne.minimum_norm.apply_inverse_cov
#Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Compute source power estimate by projecting the covariance with MNE