Assemble inverse operator.
mne.Info
The mne.Info
object with information about the sensors and methods of measurement.
Specifies the channels to include. Bad channels (in info['bads']
)
are not used.
dict
Forward operator.
Covariance
The noise covariance matrix.
float
| ‘auto’ | dict
Value that weights the source variances of the dipole components that are parallel (tangential) to the cortical surface. Can be:
If 0, then the solution is computed with fixed orientation. If 1, it corresponds to free orientations.
'auto'
(default)Uses 0.2 for surface source spaces (unless fixed
is True) and
1.0 for other source spaces (volume or mixed).
Mapping from the key for a given source space type (surface, volume, discrete) to the loose value. Useful mostly for mixed source spaces.
None
| float
| dict
How to weight (or normalize) the forward using a depth prior.
If float (default 0.8), it acts as the depth weighting exponent (exp
)
to use None is equivalent to 0, meaning no depth weighting is performed.
It can also be a dict
containing keyword arguments to pass to
mne.forward.compute_depth_prior()
(see docstring for details and
defaults). This is effectively ignored when method='eLORETA'
.
Changed in version 0.20: Depth bias ignored for method='eLORETA'
.
Use fixed source orientations normal to the cortical mantle. If True, the loose parameter must be “auto” or 0. If ‘auto’, the loose value is used.
None
| ‘info’ | ‘full’ | dict
This controls the rank computation that can be read from the measurement info or estimated from the data. When a noise covariance is used for whitening, this should reflect the rank of that covariance, otherwise amplification of noise components can occur in whitening (e.g., often during source localization).
None
The rank will be estimated from the data after proper scaling of different channel types.
'info'
The rank is inferred from info
. If data have been processed
with Maxwell filtering, the Maxwell filtering header is used.
Otherwise, the channel counts themselves are used.
In both cases, the number of projectors is subtracted from
the (effective) number of channels in the data.
For example, if Maxwell filtering reduces the rank to 68, with
two projectors the returned value will be 66.
'full'
The rank is assumed to be full, i.e. equal to the
number of good channels. If a Covariance
is passed, this can
make sense if it has been (possibly improperly) regularized without
taking into account the true data rank.
dict
Calculate the rank only for a subset of channel types, and explicitly specify the rank for the remaining channel types. This can be extremely useful if you already know the rank of (part of) your data, for instance in case you have calculated it earlier.
This parameter must be a dictionary whose keys correspond to
channel types in the data (e.g. 'meg'
, 'mag'
, 'grad'
,
'eeg'
), and whose values are integers representing the
respective ranks. For example, {'mag': 90, 'eeg': 45}
will assume
a rank of 90
and 45
for magnetometer data and EEG data,
respectively.
The ranks for all channel types present in the data, but not specified in the dictionary will be estimated empirically. That is, if you passed a dataset containing magnetometer, gradiometer, and EEG data together with the dictionary from the previous example, only the gradiometer rank would be determined, while the specified magnetometer and EEG ranks would be taken for granted.
The default is None
.
Whether to use cortical patch statistics to define normal orientations for surfaces (default True).
str
| int
| None
Control verbosity of the logging output. If None
, use the default
verbosity level. See the logging documentation and
mne.verbose()
for details. Should only be passed as a keyword
argument.
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 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.
For depth weighting, 0.8 is generally good for MEG, and between 2 and 5 is good for EEG, see [1].
References
mne.minimum_norm.make_inverse_operator
#Working with CTF data: the Brainstorm auditory dataset
Source localization with MNE, dSPM, sLORETA, and eLORETA
The role of dipole orientations in distributed source localization
Computing various MNE solutions
EEG source localization given electrode locations on an MRI
Simulate raw data using subject anatomy
Compute source power spectral density (PSD) of VectorView and OPM data
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute MNE inverse solution on evoked data with a mixed source space
Compute source power estimate by projecting the covariance with MNE
Computing source timecourses with an XFit-like multi-dipole model
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG
Compute MxNE with time-frequency sparse prior
Optically pumped magnetometer (OPM) data
From raw data to dSPM on SPM Faces dataset