mne.minimum_norm.apply_inverse#

mne.minimum_norm.apply_inverse(evoked, inverse_operator, lambda2=0.1111111111111111, method='dSPM', pick_ori=None, prepared=False, label=None, method_params=None, return_residual=False, use_cps=True, verbose=None)[source]#

Apply inverse operator to evoked data.

Parameters:
evokedEvoked object

Evoked data.

inverse_operatorinstance of InverseOperator

Inverse operator.

lambda2float

The regularization parameter.

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

Use minimum norm [1], dSPM (default) [2], sLORETA [3], or eLORETA [4].

pick_oriNone | “normal” | “vector”

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.

  • "vector"

    No pooling of the orientations is done, and the vector result will be returned in the form of a mne.VectorSourceEstimate object.

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.

New in v0.16.

return_residualbool

If True (default False), return the residual evoked data. Cannot be used with method=='eLORETA'.

New in v0.17.

use_cpsbool

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

Only used when the inverse is free orientation (loose=1.), not in surface orientation, and pick_ori='normal'.

New in v0.20.

verbosebool | 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.

Returns:
stcSourceEstimate | VectorSourceEstimate | VolSourceEstimate

The source estimates.

residualinstance of Evoked

The residual evoked data, only returned if return_residual is True.

See also

apply_inverse_raw

Apply inverse operator to raw object.

apply_inverse_epochs

Apply inverse operator to epochs object.

apply_inverse_tfr_epochs

Apply inverse operator to epochs tfr object.

apply_inverse_cov

Apply inverse operator to covariance object.

Notes

Currently only the method='eLORETA' has additional options. It performs an iterative fit with a convergence criterion, so you can pass a method_params dict with string keys mapping to values for:

‘eps’float

The convergence epsilon (default 1e-6).

‘max_iter’int

The maximum number of iterations (default 20). If less regularization is applied, more iterations may be necessary.

‘force_equal’bool

Force all eLORETA weights for each direction for a given location equal. The default is None, which means True for loose-orientation inverses and False for free- and fixed-orientation inverses. See below.

The eLORETA paper [4] defines how to compute inverses for fixed- and free-orientation inverses. In the free orientation case, the X/Y/Z orientation triplet for each location is effectively multiplied by a 3x3 weight matrix. This is the behavior obtained with force_equal=False parameter.

However, other noise normalization methods (dSPM, sLORETA) multiply all orientations for a given location by a single value. Using force_equal=True mimics this behavior by modifying the iterative algorithm to choose uniform weights (equivalent to a 3x3 diagonal matrix with equal entries).

It is necessary to use force_equal=True with loose orientation inverses (e.g., loose=0.2), otherwise the solution resembles a free-orientation inverse (loose=1.0). It is thus recommended to use force_equal=True for loose orientation and force_equal=False for free orientation inverses. This is the behavior used when the parameter force_equal=None (default behavior).

References

Examples using mne.minimum_norm.apply_inverse#

Overview of MEG/EEG analysis with MNE-Python

Overview of MEG/EEG analysis with MNE-Python

Working with CTF data: the Brainstorm auditory dataset

Working with CTF data: the Brainstorm auditory dataset

Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA

The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization

Computing various MNE solutions

Computing various MNE solutions

Visualize source time courses (stcs)

Visualize source time courses (stcs)

EEG source localization given electrode locations on an MRI

EEG source localization given electrode locations on an MRI

Permutation t-test on source data with spatio-temporal clustering

Permutation t-test on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

Decoding (MVPA)

Decoding (MVPA)

Corrupt known signal with point spread

Corrupt known signal with point spread

DICS for power mapping

DICS for power mapping

Compare simulated and estimated source activity

Compare simulated and estimated source activity

Simulate raw data using subject anatomy

Simulate raw data using subject anatomy

Compute MNE-dSPM inverse solution on single epochs

Compute MNE-dSPM inverse solution on single epochs

Compute MNE-dSPM inverse solution on evoked data in volume source space

Compute MNE-dSPM inverse solution on evoked data in volume source space

Generate a functional label from source estimates

Generate a functional label from source estimates

Extracting the time series of activations in a label

Extracting the time series of activations in a label

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space

Morph volumetric source estimate

Morph volumetric source estimate

Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model

Computing source space SNR

Computing source space SNR

Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior

Plotting the full vector-valued MNE solution

Plotting the full vector-valued MNE solution

Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data

From raw data to dSPM on SPM Faces dataset

From raw data to dSPM on SPM Faces dataset