mne.Forward#
- class mne.Forward[source]#
Forward class to represent info from forward solution.
Methods
__contains__
(key, /)True if the dictionary has the specified key, else False.
x.__getitem__(y) <==> x[y]
__iter__
(/)Implement iter(self).
__len__
(/)Return len(self).
clear
()copy
()Copy the Forward instance.
fromkeys
(iterable[, value])Create a new dictionary with keys from iterable and values set to value.
get
(key[, default])Return the value for key if key is in the dictionary, else default.
items
()keys
()pick_channels
(ch_names[, ordered])Pick channels from this forward operator.
pop
(k[,d])If key is not found, d is returned if given, otherwise KeyError is raised
popitem
(/)Remove and return a (key, value) pair as a 2-tuple.
setdefault
(key[, default])Insert key with a value of default if key is not in the dictionary.
update
([E, ]**F)If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values
()- __contains__(key, /)#
True if the dictionary has the specified key, else False.
- __getitem__()#
x.__getitem__(y) <==> x[y]
- __iter__(/)#
Implement iter(self).
- __len__(/)#
Return len(self).
- clear() None. Remove all items from D. #
- fromkeys(iterable, value=None, /)#
Create a new dictionary with keys from iterable and values set to value.
- get(key, default=None, /)#
Return the value for key if key is in the dictionary, else default.
- items() a set-like object providing a view on D's items #
- keys() a set-like object providing a view on D's keys #
- 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 version 0.20.0.
- pop(k[, d]) v, remove specified key and return the corresponding value. #
If key is not found, d is returned if given, otherwise KeyError is raised
- popitem(/)#
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.
- setdefault(key, default=None, /)#
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
- update([E, ]**F) None. Update D from dict/iterable E and F. #
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
- values() an object providing a view on D's values #
Examples using mne.Forward
#
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
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 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
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
Compute Rap-Music on evoked data
Compute spatial resolution metrics to compare MEG with EEG+MEG
Compute MxNE with time-frequency sparse prior