mne.make_sphere_model#

mne.make_sphere_model(r0=(0.0, 0.0, 0.04), head_radius=0.09, info=None, relative_radii=(0.9, 0.92, 0.97, 1.0), sigmas=(0.33, 1.0, 0.004, 0.33), verbose=None)[source]#

Create a spherical model for forward solution calculation.

Parameters:
r0array_like | str

Head center to use (in head coordinates). If ‘auto’, the head center will be calculated from the digitization points in info.

head_radiusfloat | str | None

If float, compute spherical shells for EEG using the given radius. If 'auto', estimate an appropriate radius from the dig points in the Info provided by the argument info. If None, exclude shells (single layer sphere model).

infomne.Info | None

The mne.Info object with information about the sensors and methods of measurement. Only needed if r0 or head_radius are 'auto'.

relative_radiiarray_like

Relative radii for the spherical shells.

sigmasarray_like

Sigma values for the spherical shells.

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:
sphereinstance of ConductorModel

The resulting spherical conductor model.

Notes

The default model has:

relative_radii = (0.90, 0.92, 0.97, 1.0)
sigmas = (0.33, 1.0, 0.004, 0.33)

These correspond to compartments (with relative radii in m and conductivities σ in S/m) for the brain, CSF, skull, and scalp, respectively.

New in v0.9.0.

Examples using mne.make_sphere_model#

Setting the EEG reference

Setting the EEG reference

Source alignment and coordinate frames

Source alignment and coordinate frames

Brainstorm Elekta phantom dataset tutorial

Brainstorm Elekta 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

KIT phantom dataset tutorial

KIT phantom dataset tutorial

Plot sensor denoising using oversampled temporal projection

Plot sensor denoising using oversampled temporal projection

Plotting sensor layouts of EEG systems

Plotting sensor layouts of EEG systems

Kernel OPM phantom data

Kernel OPM phantom data