mne.fit_dipole#

mne.fit_dipole(evoked, cov, bem, trans=None, min_dist=5.0, n_jobs=None, pos=None, ori=None, rank=None, accuracy='normal', tol=5e-05, verbose=None)[source]#

Fit a dipole.

Parameters
evokedinstance of Evoked

The dataset to fit.

covstr | instance of Covariance

The noise covariance.

bemstr | instance of ConductorModel

The BEM filename (str) or conductor model.

transstr | None

The head<->MRI transform filename. Must be provided unless BEM is a sphere model.

min_distfloat

Minimum distance (in millimeters) from the dipole to the inner skull. Must be positive. Note that because this is a constraint passed to a solver it is not strict but close, i.e. for a min_dist=5. the fits could be 4.9 mm from the inner skull.

n_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_backend() context manager that sets another value for n_jobs. It is used in field computation and fitting.

posndarray, shape (3,) | None

Position of the dipole to use. If None (default), sequential fitting (different position and orientation for each time instance) is performed. If a position (in head coords) is given as an array, the position is fixed during fitting.

New in version 0.12.

orindarray, shape (3,) | None

Orientation of the dipole to use. If None (default), the orientation is free to change as a function of time. If an orientation (in head coordinates) is given as an array, pos must also be provided, and the routine computes the amplitude and goodness of fit of the dipole at the given position and orientation for each time instant.

New in version 0.12.

rankNone | ‘info’ | ‘full’ | dict

This controls the rank computation that can be read from the measurement info or estimated from the data. When a noise covariance is used for whitening, this should reflect the rank of that covariance, otherwise amplification of noise components can occur in whitening (e.g., often during source localization).

None

The rank will be estimated from the data after proper scaling of different channel types.

'info'

The rank is inferred from info. If data have been processed with Maxwell filtering, the Maxwell filtering header is used. Otherwise, the channel counts themselves are used. In both cases, the number of projectors is subtracted from the (effective) number of channels in the data. For example, if Maxwell filtering reduces the rank to 68, with two projectors the returned value will be 66.

'full'

The rank is assumed to be full, i.e. equal to the number of good channels. If a Covariance is passed, this can make sense if it has been (possibly improperly) regularized without taking into account the true data rank.

dict

Calculate the rank only for a subset of channel types, and explicitly specify the rank for the remaining channel types. This can be extremely useful if you already know the rank of (part of) your data, for instance in case you have calculated it earlier.

This parameter must be a dictionary whose keys correspond to channel types in the data (e.g. 'meg', 'mag', 'grad', 'eeg'), and whose values are integers representing the respective ranks. For example, {'mag': 90, 'eeg': 45} will assume a rank of 90 and 45 for magnetometer data and EEG data, respectively.

The ranks for all channel types present in the data, but not specified in the dictionary will be estimated empirically. That is, if you passed a dataset containing magnetometer, gradiometer, and EEG data together with the dictionary from the previous example, only the gradiometer rank would be determined, while the specified magnetometer and EEG ranks would be taken for granted.

The default is None.

New in version 0.20.

accuracystr

Can be “normal” (default) or “accurate”, which gives the most accurate coil definition but is typically not necessary for real-world data.

New in version 0.24.

tolfloat

Final accuracy of the optimization (see rhoend argument of scipy.optimize.fmin_cobyla()).

New in version 0.24.

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
dipinstance of Dipole or DipoleFixed

The dipole fits. A mne.DipoleFixed is returned if pos and ori are both not None, otherwise a mne.Dipole is returned.

residualinstance of Evoked

The M-EEG data channels with the fitted dipolar activity removed.

Notes

New in version 0.9.0.

Examples using mne.fit_dipole#

Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit
Visualize source time courses (stcs)

Visualize source time courses (stcs)

Visualize source time courses (stcs)
Brainstorm Elekta phantom dataset tutorial

Brainstorm Elekta phantom dataset tutorial

Brainstorm Elekta phantom dataset tutorial
Brainstorm CTF phantom dataset tutorial

Brainstorm CTF phantom dataset tutorial

Brainstorm CTF phantom dataset tutorial
4D Neuroimaging/BTi phantom dataset tutorial

4D Neuroimaging/BTi phantom dataset tutorial

4D Neuroimaging/BTi phantom dataset tutorial
Plot sensor denoising using oversampled temporal projection

Plot sensor denoising using oversampled temporal projection

Plot sensor denoising using oversampled temporal projection
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
Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data