mne.compute_morph_matrix¶

mne.compute_morph_matrix(subject_from, subject_to, vertices_from, vertices_to, smooth=None, subjects_dir=None, warn=True, verbose=None)[source]¶

Get a matrix that morphs data from one subject to another.

Parameters:

subject_from : string Name of the original subject as named in the SUBJECTS_DIR subject_to : string Name of the subject on which to morph as named in the SUBJECTS_DIR vertices_from : list of arrays of int Vertices for each hemisphere (LH, RH) for subject_from vertices_to : list of arrays of int Vertices for each hemisphere (LH, RH) for subject_to smooth : int or None Number of iterations for the smoothing of the surface data. If None, smooth is automatically defined to fill the surface with non-zero values. subjects_dir : string Path to SUBJECTS_DIR is not set in the environment warn : bool If True, warn if not all vertices were used. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

morph_matrix : sparse matrix matrix that morphs data from subject_from to subject_to

Examples using mne.compute_morph_matrix¶

Morph source estimates from one subject to another subject

Source localization with MNE/dSPM/sLORETA

Permutation t-test on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering