- raw : Raw object
Raw data.
- inverse_operator : dict
Inverse operator.
- lambda2 : float
The regularization parameter.
- method : “MNE” | “dSPM” | “sLORETA” | “eLORETA”
Use minimum norm, dSPM (default), sLORETA, or eLORETA.
- label : Label | None
Restricts the source estimates to a given label. If None,
source estimates will be computed for the entire source space.
- start : int
Index of first time sample (index not time is seconds).
- stop : int
Index of first time sample not to include (index not time is seconds).
- nave : int
Number of averages used to regularize the solution.
Set to 1 on raw data.
- time_func : callable
Linear function applied to sensor space time series.
- 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 does not work when using
an inverse operator with fixed orientations.
- buffer_size : int (or None)
If not None, the computation of the inverse and the combination of the
current components is performed in segments of length buffer_size
samples. While slightly slower, this is useful for long datasets as it
reduces the memory requirements by approx. a factor of 3 (assuming
buffer_size << data length).
Note that this setting has no effect for fixed-orientation inverse
operators.
- prepared : bool
If True, do not call prepare_inverse_operator().
- method_params : dict | None
Additional options for eLORETA. See Notes of apply_inverse().
- verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose()
and Logging documentation for more).
