- mne.read_forward_solution(fname, include=(), exclude=(), *, ordered=None, verbose=None)[source]#
Read a forward solution a.k.a. lead field.
The file name, which should end with
List of names of channels to include. If empty all channels are included.
List of names of channels to exclude. If empty include all channels.
If True (default False), ensure that the order of the channels in the modified instance matches the order of
New in version 0.20.0.
Changed in version 1.5: The default changed from False in 1.4 to True in 1.5.
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.
- fwdinstance of
The forward solution.
- fwdinstance of
Forward solutions, which are derived from an original forward solution with free orientation, are always stored on disk as forward solution with free orientation in X/Y/Z RAS coordinates. To apply any transformation to the forward operator (surface orientation, fixed orientation) please apply
convert_forward_solution()after reading the forward solution with
Forward solutions, which are derived from an original forward solution with fixed orientation, are stored on disk as forward solution with fixed surface-based orientations. Please note that the transformation to surface-based, fixed orientation cannot be reverted after loading the forward solution with
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
Source reconstruction using an LCMV beamformer
Corrupt known signal with point spread
Compare simulated and estimated source activity
Generate simulated evoked data
Simulate raw data using subject anatomy
Generate simulated source data
Cortical Signal Suppression (CSS) for removal of cortical signals
Sensitivity map of SSP projections
Display sensitivity maps for EEG and MEG sensors
Source localization with a custom inverse solver
Compute source level time-frequency timecourses using a DICS beamformer
Compute source power using DICS beamformer
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute source power estimate by projecting the covariance with MNE
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary
Visualize source leakage among labels using a circular graph
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers
Compute Rap-Music on evoked data
Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG
Compute MxNE with time-frequency sparse prior
Compute Trap-Music on evoked data
Optically pumped magnetometer (OPM) data