mne.minimum_norm.apply_inverse_epochs¶

mne.minimum_norm.apply_inverse_epochs(epochs, inverse_operator, lambda2, method='dSPM', label=None, nave=1, pick_ori=None, return_generator=False, prepared=False, method_params=None, use_cps=True, verbose=None)[source]¶

Apply inverse operator to Epochs.

Parameters

epochsEpochs object

Single trial epochs.

inverse_operatordict

Inverse operator.

lambda2float

The regularization parameter.

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

Use minimum norm, dSPM (default), sLORETA, or eLORETA.

labelLabel | None

Restricts the source estimates to a given label. If None, source estimates will be computed for the entire source space.

naveint

Number of averages used to regularize the solution. Set to 1 on single Epoch by default.

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.

return_generatorbool

Return a generator object instead of a list. This allows iterating over the stcs without having to keep them all in memory.

preparedbool

If True, do not call prepare_inverse_operator().

method_paramsdict | None

Additional options for eLORETA. See Notes of apply_inverse().

New in version 0.16.

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 version 0.20.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Returns