# mne.minimum_norm.apply_inverse_raw¶

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, verbose=None)[source]

Apply inverse operator to Raw data.

Parameters: raw : Raw object Raw data. inverse_operator : dict Inverse operator. lambda2 : float The regularization parameter. method : “MNE” | “dSPM” | “sLORETA” | “eLORETA” Use mininum 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(). New in version 0.16. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more). stc : SourceEstimate | VectorSourceEstimate | VolSourceEstimate The source estimates.

apply_inverse_epochs
apply_inverse