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:

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” 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. 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 \| VolSourceEstimate The source estimates

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”

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.

prepared : bool