mne.make_bem_model#

mne.make_bem_model(subject, ico=4, conductivity=(0.3, 0.006, 0.3), subjects_dir=None, verbose=None)[source]#

Create a BEM model for a subject.

Note

To get a single layer bem corresponding to the –homog flag in the command line tool set the conductivity parameter to a list/tuple with a single value (e.g. [0.3]).

Parameters:

subjectstr: The subject.
icoint | None: The surface ico downsampling to use, e.g. 5=20484, 4=5120, 3=1280. If None, no subsampling is applied.
conductivityarray of int, shape (3,) or (1,): The conductivities to use for each shell. Should be a single element for a one-layer model, or three elements for a three-layer model. Defaults to [0.3, 0.006, 0.3]. The MNE-C default for a single-layer model would be [0.3].
subjects_dirpath-like | None: The path to the directory containing the FreeSurfer subjects reconstructions. If None, defaults to the SUBJECTS_DIR environment variable.
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:

surfaceslist of dict: The BEM surfaces. Use make_bem_solution to turn these into a ConductorModel suitable for forward calculation.

Examples using `mne.make_bem_model`#

Working with CTF data: the Brainstorm auditory dataset

Head model and forward computation

Fixing BEM and head surfaces

mne.make_bem_model#

Examples using mne.make_bem_model#

Examples using `mne.make_bem_model`#