Calculate a forward solution for a subject.
mne.Info
| path-likeThe mne.Info
object with information about the sensors and methods of measurement. If path-like
, it should be a str
or
pathlib.Path
to a file with measurement information
(e.g. mne.io.Raw
).
dict
| instance of Transform
| None
If str, the path to the head<->MRI transform *-trans.fif
file produced
during coregistration. Can also be 'fsaverage'
to use the built-in
fsaverage transformation.
If trans is None, an identity matrix is assumed.
Changed in version 0.19: Support for ‘fsaverage’ argument.
SourceSpaces
If string, should be a source space filename. Can also be an instance of loaded or generated SourceSpaces.
dict
Filename of the BEM (e.g., “sample-5120-5120-5120-bem-sol.fif”) to use, or a loaded sphere model (dict).
If True (Default), include MEG computations.
If True (Default), include EEG computations.
float
Minimum distance of sources from inner skull surface (in mm).
If True, do not include reference channels in compensation. This option should be True for KIT files, since forward computation with reference channels is not currently supported.
int
| None
The number of jobs to run in parallel. If -1
, it is set
to the number of CPU cores. Requires the joblib
package.
None
(default) is a marker for ‘unset’ that will be interpreted
as n_jobs=1
(sequential execution) unless the call is performed under
a joblib.parallel_backend()
context manager that sets another
value for n_jobs
.
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.
Forward
The forward solution.
See also
Notes
The --grad
option from MNE-C (to compute gradients) is not implemented
here.
To create a fixed-orientation forward solution, use this function
followed by mne.convert_forward_solution()
.
mne.make_forward_solution
#Working with CTF data: the Brainstorm auditory dataset
Head model and forward computation
EEG forward operator with a template MRI
EEG source localization given electrode locations on an MRI
Compute source power spectral density (PSD) of VectorView and OPM data
Compute MNE inverse solution on evoked data with a mixed source space
From raw data to dSPM on SPM Faces dataset