mne.Forward#
- class mne.Forward[source]#
Forward class to represent info from forward solution.
Like
mne.Info
, this data structure behaves like a dictionary. It contains all metadata necessary for a forward solution.Warning
This class should not be modified or created by users. Forward objects should be obtained using
mne.make_forward_solution()
ormne.read_forward_solution()
.Notes
Forward data is accessible via string keys using standard
dict
access (e.g.,fwd['nsource'] == 4096
):- source_oriint
The source orientation, either
FIFF.FIFFV_MNE_FIXED_ORI
orFIFF.FIFFV_MNE_FREE_ORI
.- coord_frameint
The coordinate frame of the forward solution, usually
FIFF.FIFFV_COORD_HEAD
.- nsourceint
The number of source locations.
- nchanint
The number of channels.
- soldict
The forward solution, with entries:
'data'
ndarray, shape (n_channels, nsource * n_ori)The forward solution data. The shape will be
(n_channels, nsource)
for a fixed-orientation forward and(n_channels, nsource * 3)
for a free-orientation forward.'row_names'
list of strThe channel names.
- mri_head_tinstance of Transform
The mri ↔ head transformation that was used.
- infoinstance of
Info
The measurement information (with contents reduced compared to that of the original data).
- srcinstance of
SourceSpaces
The source space used during forward computation. This can differ from the original source space as:
Source points are removed due to proximity to (or existing outside) the inner skull surface.
The source space will be converted to the
coord_frame
of the forward solution, which typically means it gets converted from MRI to head coordinates.
- source_rrndarray, shape (n_sources, 3)
The source locations.
- source_nnndarray, shape (n_sources, 3)
The source normals. Will be all +Z (
(0, 0, 1.)
) for volume source spaces. For surface source spaces, these are normal to the cortical surface.- surf_oriint
Whether
sol
is surface-oriented with the surface normal in the Z component (FIFF.FIFFV_MNE_FIXED_ORI
) or +Z in the givencoord_frame
in the Z component (FIFF.FIFFV_MNE_FREE_ORI
).
Forward objects also have some attributes that are accessible via
.
access, likefwd.ch_names
.- Attributes:
Methods
copy
()Copy the Forward instance.
pick_channels
(ch_names[, ordered])Pick channels from this forward operator.
save
(fname, *[, overwrite, verbose])Save the forward solution.
- pick_channels(ch_names, ordered=False)[source]#
Pick channels from this forward operator.
- Parameters:
- Returns:
- fwdinstance of Forward.
The modified forward model.
Notes
Operates in-place.
New in v0.20.0.
- save(fname, *, overwrite=False, verbose=None)[source]#
Save the forward solution.
- Parameters:
- fnamepath-like
File name to save the forward solution to. It should end with
-fwd.fif
or-fwd.fif.gz
to save to FIF, or-fwd.h5
to save to HDF5.- overwritebool
If True (default False), overwrite the destination file if it exists.
- 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.
Examples using mne.Forward
#
Getting started with mne.Report
Head model and forward computation
EEG forward operator with a template MRI
Source localization with equivalent current dipole (ECD) fit
The role of dipole orientations in distributed source localization
EEG source localization given electrode locations on an MRI
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 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
Computing source timecourses with an XFit-like multi-dipole model
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers
Plot point-spread functions (PSFs) for a volume
Compute Rap-Music on evoked data
Compute spatial resolution metrics to compare MEG with EEG+MEG
Compute MxNE with time-frequency sparse prior
Compute Trap-Music on evoked data