mne.minimum_norm.
make_inverse_operator
(info, forward, noise_cov, loose='auto', depth=0.8, fixed='auto', limit_depth_chs=True, rank=None, use_cps=None, verbose=None)[source]¶Assemble inverse operator.
Parameters:  info : dict
forward : dict
noise_cov : instance of Covariance
loose : float in [0, 1]  ‘auto’
depth : None  float in [0, 1]
fixed : bool  ‘auto’
limit_depth_chs : bool
rank : None  int  dict
use_cps : None  bool (default None)
verbose : bool, str, int, or None


Returns:  inv : instance of InverseOperator

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 weighted0.2 0.8 False False True Loose constraint0.2 None False False True Free orientation,Depth weighted1.0 0.8 False False True Free orientation1.0 None False False True  False Fixed constraint,Depth weighted0.0 0.8 True False True Fixed constraint0.0 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.