API Reference

This is the reference for classes (CamelCase names) and functions (underscore_case names) of Sesameeg.

Main class

Sesame(source_space, lead_field, data[, ...])

Sequential Semi-Analytic Monte-Carlo Estimator (SESAME).

MNE-Python utility functions

prepare_sesame(forward, data[, n_parts, ...])

Prepare a SESAME instance for actually computing the inverse.

Visualization

plot_n_sources(inv_op[, kind, title])	Plot the probability of number of sources.
plot_stc(inv_op[, plot_kwargs, savepath, ...])	Plot SESAME source estimates using mne.
plot_vol_stc(inv_op[, plot_kwargs, ...])	Plot Nutmeg style SESAME volumetric source estimates using nilearn.
plot_cloud_sources(inv_op[, savepath, true_idxs])	Plot point cloud style SESAME source estimates using pyvista.
plot_amplitudes(inv_op[, title])	Plot the amplitude of the estimated sources as function of time.

Reading SESAME result

read_h5(fpath)

Load SESAME result from an HDF5 file.

Other classes

Dipole(loc)	Single current dipole class for SESAME.
Particle(n_verts, lam[, dip_mom_std, ...])	Particle class for SESAME, used to store a single particle of an empirical pdf.
EmpPdf(n_parts, n_verts, lam[, dip_mom_std, ...])	Empirical probability density function (pdf) class for SESAME.

Utility functions

prior_loc_from_labels(subject, subjects_dir, ...)	Construct the prior probability of active source locations starting from given FreeSurfer/MNE labels.
compute_neighbours_matrix(src, d_matrix, ...)	Compute the set of neighbours of each point in the brain discretization.
compute_neighbours_probability_matrix(...)	Compute neighbours' probability matrix.
estimate_dip_mom_std(r_data, lf)	Estimate the standard deviation of the prior of the dipole moment.
estimate_noise_std(r_data)	Estimate the standard deviation of noise distribution.
initialize_radius(src)	Guess the units of the points in the brain discretization and set to 1 cm the value of the radius for computing the sets of neighbours.