mne.read_forward_solution#
- mne.read_forward_solution(fname, include=(), exclude=(), *, ordered=True, verbose=None)[source]#
Read a forward solution a.k.a. lead field.
- Parameters:
- fnamepath-like
The file name, which should end with
-fwd.fif
,-fwd.fif.gz
,_fwd.fif
,_fwd.fif.gz
,-fwd.h5
, or_fwd.h5
.- include
list
, optional List of names of channels to include. If empty all channels are included.
- exclude
list
, optional List of names of channels to exclude. If empty include all channels.
- orderedbool
If True (default), ensure that the order of the channels in the modified instance matches the order of
ch_names
.New in v0.20.0.
Changed in version 1.7: The default changed from False in 1.6 to True in 1.7.
- verbosebool |
str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- Returns:
- fwdinstance of
Forward
The forward solution.
- fwdinstance of
See also
Notes
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 withread_forward_solution()
.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
read_forward_solution()
.
Examples using mne.read_forward_solution
#
![](../_images/sphx_glr_60_ctf_bst_auditory_thumb.png)
Working with CTF data: the Brainstorm auditory dataset
![](../_images/sphx_glr_30_mne_dspm_loreta_thumb.gif)
Source localization with MNE, dSPM, sLORETA, and eLORETA
![](../_images/sphx_glr_35_dipole_orientations_thumb.png)
The role of dipole orientations in distributed source localization
![](../_images/sphx_glr_css_thumb.png)
Cortical Signal Suppression (CSS) for removal of cortical signals
![](../_images/sphx_glr_dics_epochs_thumb.png)
Compute source level time-frequency timecourses using a DICS beamformer
![](../_images/sphx_glr_evoked_ers_source_power_thumb.png)
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
![](../_images/sphx_glr_gamma_map_inverse_thumb.png)
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
![](../_images/sphx_glr_mixed_norm_inverse_thumb.png)
Compute sparse inverse solution with mixed norm: MxNE and irMxNE
![](../_images/sphx_glr_mne_cov_power_thumb.png)
Compute source power estimate by projecting the covariance with MNE
![](../_images/sphx_glr_multidict_reweighted_tfmxne_thumb.png)
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary
![](../_images/sphx_glr_psf_ctf_label_leakage_thumb.png)
Visualize source leakage among labels using a circular graph
![](../_images/sphx_glr_psf_ctf_vertices_thumb.png)
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
![](../_images/sphx_glr_resolution_metrics_thumb.png)
Compute spatial resolution metrics in source space
![](../_images/sphx_glr_resolution_metrics_eegmeg_thumb.png)
Compute spatial resolution metrics to compare MEG with EEG+MEG