mne.read_forward_solution

mne.read_forward_solution(fname, include=(), exclude=(), verbose=None)

Read a forward solution a.k.a. lead field.

Parameters:

fnamestr

The file name, which should end with -fwd.fif or -fwd.fif.gz.

includelist, optional

List of names of channels to include. If empty all channels are included.

excludelist, optional

List of names of channels to exclude. If empty include all channels.

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.

See also

write_forward_solution, make_forward_solution

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 with read_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

Working with CTF data: the Brainstorm auditory dataset

Working with CTF data: the Brainstorm auditory dataset

Head model and forward computation

Head model and forward computation

Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA

The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization

Computing various MNE solutions

Computing various MNE solutions

Source reconstruction using an LCMV beamformer

Source reconstruction using an LCMV beamformer

Decoding (MVPA)

Decoding (MVPA)

Corrupt known signal with point spread

Corrupt known signal with point spread

DICS for power mapping

DICS for power mapping

Compare simulated and estimated source activity

Compare simulated and estimated source activity

Generate simulated evoked data

Generate simulated evoked data

Generate simulated raw data

Generate simulated raw data

Simulate raw data using subject anatomy

Simulate raw data using subject anatomy

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

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

Source localization with a custom inverse solver

Source localization with a custom inverse solver

Compute source power using DICS beamformer

Compute source power using 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 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 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

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

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

Visualize source leakage among labels using a circular graph

Visualize source leakage among labels using a circular graph

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 Rap-Music on evoked data

Compute Rap-Music on evoked data

Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space

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

Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior

Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data