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)

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

[1]

Matti S. Hämäläinen and Ilmoniemi Ilmoniemi, Risto J. Interpreting magnetic fields of the brain: minimum norm estimates. Medical & Biological Engineering & Computing, 32(1):35–42, 1994. doi:10.1007/BF02512476.

[2]

Anders M. Dale, Arthur K. Liu, Bruce R. Fischl, Randy L. Buckner, John W. Belliveau, Jeffrey D. Lewine, and Eric Halgren. Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron, 26(1):55–67, 2000. doi:10.1016/S0896-6273(00)81138-1.

[3]

Roberto D. Pascual-Marqui. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods and Findings in Experimental and Clinical Pharmacology, 24(D):5–12, 2002. URL: https://pubmed.ncbi.nlm.nih.gov/12575463/.

[4] (1,2)

Roberto D. Pascual-Marqui, Dietrich Lehmann, Martha Koukkou, Kieko Kochi, Peter Anderer, Bernd Saletu, Hideaki Tanaka, Koichi Hirata, E. Roy John, Leslie Prichep, Rolando Biscay-Lirio, and Toshihiko Kinoshita. Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369(1952):3768–3784, 2011. doi:10.1098/rsta.2011.0081.

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