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() or mne.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 or FIFF.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 str

The 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:

  1. Source points are removed due to proximity to (or existing outside) the inner skull surface.

  2. 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 given coord_frame in the Z component (FIFF.FIFFV_MNE_FREE_ORI).

Forward objects also have some attributes that are accessible via . access, like fwd.ch_names.

Attributes:
ch_nameslist of str

A convenience wrapper accessible as fwd.ch_names which wraps fwd['info']['ch_names'].

Methods

copy()

Copy the Forward instance.

pick_channels(ch_names[, ordered])

Pick channels from this forward operator.

copy()[source]#

Copy the Forward instance.

pick_channels(ch_names, ordered=False)[source]#

Pick channels from this forward operator.

Parameters:
ch_nameslist of str

List of channels to include.

orderedbool

If true (default False), treat include as an ordered list rather than a set.

Returns:
fwdinstance of Forward.

The modified forward model.

Notes

Operates in-place.

New in version 0.20.0.

Examples using mne.Forward#

Setting the EEG reference

Setting the EEG reference

Setting the EEG reference
Head model and forward computation

Head model and forward computation

Head model and forward computation
EEG forward operator with a template MRI

EEG forward operator with a template MRI

EEG forward operator with a template MRI
Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit
The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization
EEG source localization given electrode locations on an MRI

EEG source localization given electrode locations on an MRI

EEG source localization given electrode locations on an MRI
Corrupt known signal with point spread

Corrupt known signal with point spread

Corrupt known signal with point spread
DICS for power mapping

DICS for power mapping

DICS for power mapping
Compare simulated and estimated source activity

Compare simulated and estimated source activity

Compare simulated and estimated source activity
Generate simulated evoked data

Generate simulated evoked data

Generate simulated evoked data
Generate simulated raw data

Generate simulated raw data

Generate simulated raw data
Simulate raw data using subject anatomy

Simulate raw data using subject anatomy

Simulate raw data using subject anatomy
Generate simulated source data

Generate simulated source data

Generate simulated source data
Cortical Signal Suppression (CSS) for removal of cortical signals

Cortical Signal Suppression (CSS) for removal of cortical signals

Cortical Signal Suppression (CSS) for removal of cortical signals
Sensitivity map of SSP projections

Sensitivity map of SSP projections

Sensitivity map of SSP projections
Display sensitivity maps for EEG and MEG sensors

Display sensitivity maps for EEG and MEG sensors

Display sensitivity maps for EEG and MEG sensors
Use source space morphing

Use source space morphing

Use source space morphing
Source localization with a custom inverse solver

Source localization with a custom inverse solver

Source localization with a custom inverse solver
Compute source level time-frequency timecourses using a DICS beamformer

Compute source level time-frequency timecourses using a DICS beamformer

Compute source level time-frequency timecourses using a DICS beamformer
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE
Morph surface source estimate

Morph surface source estimate

Morph surface source estimate
Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary

Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary

Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers
Plot point-spread functions (PSFs) for a volume

Plot point-spread functions (PSFs) for a volume

Plot point-spread functions (PSFs) for a volume
Compute Rap-Music on evoked data

Compute Rap-Music on evoked data

Compute Rap-Music on evoked data
Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG
Computing source space SNR

Computing source space SNR

Computing source space SNR
Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior