mne.make_forward_solution#

mne.make_forward_solution(info, trans, src, bem, meg=True, eeg=True, *, mindist=0.0, ignore_ref=False, n_jobs=None, verbose=None)[source]#

Calculate a forward solution for a subject.

Parameters:

infomne.Info | path-like: The 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).
transpath-like | 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.
srcpath-like | instance of SourceSpaces: Either a path to a source space file or a loaded or generated SourceSpaces.
bempath-like | dict: Filename of the BEM (e.g., "sample-5120-5120-5120-bem-sol.fif") to use, or a loaded sphere model (dict).
megbool: If True (Default), include MEG computations.
eegbool: If True (Default), include EEG computations.
mindistfloat: Minimum distance of sources from inner skull surface (in mm).
ignore_refbool: 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.
n_jobsint | 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.
verbosebool | 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.

Returns:

fwdinstance of Forward: The forward solution.

Examples using `mne.make_forward_solution`#

Working with CTF data: the Brainstorm auditory dataset

Setting the EEG reference

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

Use source space morphing

Compute MNE inverse solution on evoked data with a mixed source space

Plot point-spread functions (PSFs) for a volume

From raw data to dSPM on SPM Faces dataset

mne.make_forward_solution#

Examples using mne.make_forward_solution#

Examples using `mne.make_forward_solution`#