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mne.read_forward_solution¶

mne.read_forward_solution(fname, include=(), exclude=(), verbose=None)[source]¶

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/eLORETA — Source localization with MNE/dSPM/sLORETA/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¶

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¶

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