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, verbose=None)[source]

Apply inverse operator to Raw data.


raw : Raw object

Raw data.

inverse_operator : dict

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.

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”

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.

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.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).


stc : SourceEstimate | VolSourceEstimate

The source estimates.

See also

Apply inverse operator to epochs object
Apply inverse operator to evoked object

Examples using mne.minimum_norm.apply_inverse_raw