Weighted Resolution Optimization#
- class invert.solvers.wrop.SolverBackusGilbert(name='Backus-Gilbert', **kwargs)#
Class for the Backus Gilbert inverse solution.
- make_inverse_operator(forward, *args, alpha='auto', **kwargs)#
Calculate inverse operator.
- Parameters
forward (mne.Forward) – The mne-python Forward model instance.
alpha (float) – The regularization parameter.
- Returns
self
- Return type
object returns itself for convenience
- class invert.solvers.wrop.SolverLAURA(name='Local Auto-Regressive Average', **kwargs)#
Class for the Local AUtoRegressive Average (LAURA) inverse solution.
- make_inverse_operator(forward, *args, noise_cov=None, alpha='auto', drop_off=2, verbose=0, **kwargs)#
Calculate inverse operator.
- Parameters
forward (mne.Forward) – The mne-python Forward model instance.
alpha (float) – The regularization parameter.
noise_cov (numpy.ndarray) – The noise covariance matrix
drop_off (float) – Controls the steepness of the patches distribution. It is not adviced to change this parameter in most cases.
- Returns
self
- Return type
object returns itself for convenience