- mne.minimum_norm.apply_inverse_raw(raw, inverse_operator, lambda2, method='dSPM', label=None, start=None, stop=None, nave=1, time_func=None, pick_ori=None, buffer_size=None, prepared=False, method_params=None, use_cps=True, verbose=None)#
Apply inverse operator to Raw data.
The regularization parameter.
- method“MNE” | “dSPM” | “sLORETA” | “eLORETA”
Use minimum norm, dSPM (default), sLORETA, or eLORETA.
Restricts the source estimates to a given label. If None, source estimates will be computed for the entire source space.
Index of first time sample (index not time is seconds).
Index of first time sample not to include (index not time is seconds).
Number of averages used to regularize the solution. Set to 1 on raw data.
Linear function applied to sensor space time series.
None| “normal” | “vector”
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.
Only the normal to the cortical surface is kept. This is only implemented when working with loose orientations.
No pooling of the orientations is done, and the vector result will be returned in the form of a
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.
If True, do not call
Additional options for eLORETA. See Notes of
New in version 0.16.
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
New in version 0.20.