EEG parametric leadfield computation - On regular grid

This example will not be explained as all the required information is already available in the other tutorials. It simply shows that the regular grid source space can be used for the parametric solution.

from shamo import FEM

model = FEM.load("../../derivatives/fem_from_labels/fem_from_labels.json")

from shamo.eeg import ProbParamEEGLeadfield

problem = ProbParamEEGLeadfield()

from shamo import DistUniform

problem.sigmas.set("scalp", DistUniform(0.137, 2.1))
problem.sigmas.set("gm", DistUniform(0.06, 2.47))
problem.sigmas.set("wm", DistUniform(0.0646, 0.81))

problem.reference.add("IZ")

problem.markers.adds(["NZ", "LeftEar", "RightEar"])

import nibabel as nib
from nilearn.image import crop_img

img = nib.load(model.nii_path)
mask = img.get_fdata() == 2
mask_img = nib.Nifti1Image(mask.astype(int), img.affine)
mask_img = crop_img(mask_img)

problem.grid.set(
    mask_img.affine,
    mask_img.shape,
    mask=mask_img.get_fdata().astype(bool)
)

solution = problem.solve("parametric_ssp-grid", "../../derivatives/eeg_leadfield", model, n_evals=4, method="multiprocessing", n_proc=4)