mne.minimum_norm.make_inverse_operator¶

mne.minimum_norm.make_inverse_operator(info, forward, noise_cov, loose=0.2, depth=0.8, fixed=False, limit_depth_chs=True, rank=None, verbose=None)[source]¶

Assemble inverse operator.

Parameters:

Parameters:	info : dict The measurement info to specify the channels to include. Bad channels in info[‘bads’] are not used. forward : dict Forward operator. noise_cov : instance of Covariance The noise covariance matrix. loose : None \| float in [0, 1] Value that weights the source variances of the dipole components defining the tangent space of the cortical surfaces. Requires surface- based, free orientation forward solutions. depth : None \| float in [0, 1] Depth weighting coefficients. If None, no depth weighting is performed. fixed : bool Use fixed source orientations normal to the cortical mantle. If True, the loose parameter is ignored. limit_depth_chs : bool If True, use only grad channels in depth weighting (equivalent to MNE C code). If grad chanels aren’t present, only mag channels will be used (if no mag, then eeg). If False, use all channels. rank : None \| int \| dict Specified rank of the noise covariance matrix. If None, the rank is detected automatically. If int, the rank is specified for the MEG channels. A dictionary with entries ‘eeg’ and/or ‘meg’ can be used to specify the rank for each modality. verbose : bool, str, int, or None If not None, override default verbose level (see `mne.verbose()`).
Returns:	inv : instance of InverseOperator Inverse operator.

info : dict

The measurement info to specify the channels to include. Bad channels in info[‘bads’] are not used.

forward : dict

Forward operator.

noise_cov : instance of Covariance

The noise covariance matrix.

loose : None | float in [0, 1]

Value that weights the source variances of the dipole components defining the tangent space of the cortical surfaces. Requires surface- based, free orientation forward solutions.

depth : None | float in [0, 1]

Depth weighting coefficients. If None, no depth weighting is performed.

fixed : bool

Use fixed source orientations normal to the cortical mantle. If True, the loose parameter is ignored.

limit_depth_chs : bool

If True, use only grad channels in depth weighting (equivalent to MNE C code). If grad chanels aren’t present, only mag channels will be used (if no mag, then eeg). If False, use all channels.

rank : None | int | dict

Specified rank of the noise covariance matrix. If None, the rank is detected automatically. If int, the rank is specified for the MEG channels. A dictionary with entries ‘eeg’ and/or ‘meg’ can be used to specify the rank for each modality.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose()).

Returns:

inv : instance of InverseOperator

Inverse operator.

Notes

For different sets of options (loose, depth, fixed) to work, the forward operator must have been loaded using a certain configuration (i.e., with force_fixed and surf_ori set appropriately). For example, given the desired inverse type (with representative choices of loose = 0.2 and depth = 0.8 shown in the table in various places, as these are the defaults for those parameters):

Inverse desired Forward parameters allowed

loose depth fixed force_fixed surf_ori

Loose constraint,

Depth weighted

0.2 0.8 False False True

Loose constraint

0.2 None False False True

Free orientation,

Depth weighted

None 0.8 False False True

Free orientation

None None False False True | False

Fixed constraint,

Depth weighted

None 0.8 True False True

Fixed constraint

None None True True True

Inverse desired	Forward parameters allowed
	loose	depth	fixed	force_fixed	surf_ori
Loose constraint, Depth weighted	0.2	0.8	False	False	True
Loose constraint	0.2	None	False	False	True
Free orientation, Depth weighted	None	0.8	False	False	True
Free orientation	None	None	False	False	True \| False
Fixed constraint, Depth weighted	None	0.8	True	False	True
Fixed constraint	None	None	True	True	True

Also note that, if the source space (as stored in the forward solution) has patch statistics computed, these are used to improve the depth weighting. Thus slightly different results are to be expected with and without this information.