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, verbose=None)[source]

Apply inverse operator to evoked data.

Parameters:

evoked : Evoked object

Evoked data.

inverse_operator: instance of InverseOperator

Inverse operator returned from mne.read_inverse_operator, prepare_inverse_operator or make_inverse_operator.

lambda2 : float

The regularization parameter.

method : “MNE” | “dSPM” | “sLORETA”

Use mininum norm, dSPM or sLORETA.

pick_ori : None | “normal” | “vector”

If “normal”, rather than pooling the orientations by taking the norm, only the radial component is kept. This is only implemented when working with loose orientations. If “vector”, no pooling of the orientations is done and the vector result will be returned in the form of a mne.VectorSourceEstimate object. This is only implemented when working with loose orientations.

prepared : bool

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.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

stc : SourceEstimate | VectorSourceEstimate | VolSourceEstimate

The source estimates

See also

apply_inverse_raw
Apply inverse operator to raw object
apply_inverse_epochs
Apply inverse operator to epochs object