"""Test the ieeg projection functions.""" # Authors: The MNE-Python contributors. # License: BSD-3-Clause # Copyright the MNE-Python contributors. import os from shutil import copyfile import numpy as np import pytest from numpy.testing import assert_allclose import mne from mne.datasets import testing from mne.preprocessing.ieeg import project_sensors_onto_brain from mne.preprocessing.ieeg._projection import _project_sensors_onto_inflated from mne.transforms import _get_trans data_path = testing.data_path(download=False) subjects_dir = data_path / "subjects" fname_trans = data_path / "MEG" / "sample" / "sample_audvis_trunc-trans.fif" fname_raw = data_path / "MEG" / "sample" / "sample_audvis_trunc_raw.fif" @testing.requires_testing_data def test_project_sensors_onto_brain(tmp_path): """Test projecting sensors onto the brain surface.""" pytest.importorskip("nibabel") raw = mne.io.read_raw_fif(fname_raw) trans = _get_trans(fname_trans)[0] # test informative error for no surface first with pytest.raises(RuntimeError, match="requires generating a BEM"): project_sensors_onto_brain(raw.info, trans, "sample", subjects_dir=tmp_path) brain_surf_fname = tmp_path / "sample" / "bem" / "brain.surf" if not brain_surf_fname.parent.is_dir(): os.makedirs(brain_surf_fname.parent) if not brain_surf_fname.is_file(): copyfile( subjects_dir / "sample" / "bem" / "inner_skull.surf", brain_surf_fname, ) # now make realistic ECoG grid raw.pick("eeg") raw.load_data() raw.set_eeg_reference([]) raw.set_channel_types({ch: "ecog" for ch in raw.ch_names}) pos = np.zeros((49, 3)) pos[:, :2] = ( np.array(np.meshgrid(np.linspace(0, 0.02, 7), np.linspace(0, 0.02, 7))) .reshape(2, -1) .T ) pos[:, 2] = 0.12 raw.drop_channels(raw.ch_names[49:]) raw.set_montage( mne.channels.make_dig_montage( ch_pos=dict(zip(raw.ch_names[:49], pos)), coord_frame="head" ) ) raw.info = project_sensors_onto_brain( raw.info, trans, "sample", subjects_dir=tmp_path ) # plot to check, should be projected down onto inner skull # brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, alpha=0.5, # surf='white') # brain.add_sensors(raw.info, trans=trans) test_locs = [ [0.00149, -0.001588, 0.133029], [0.004302, 0.001959, 0.133922], [0.008602, 0.00116, 0.133723], ] montage = raw.get_montage() assert montage is not None ch_pos = montage.get_positions()["ch_pos"] for ch, test_loc in zip(raw.ch_names[:3], test_locs): assert_allclose(ch_pos[ch], test_loc, atol=0.01) @testing.requires_testing_data def test_project_sensors_onto_inflated(tmp_path): """Test projecting sEEG sensors onto an inflated brain surface.""" pytest.importorskip("nibabel") raw = mne.io.read_raw_fif(fname_raw) trans = _get_trans(fname_trans)[0] for subject in ("sample", "fsaverage"): os.makedirs(tmp_path / subject / "surf", exist_ok=True) for hemi in ("lh", "rh"): # fake white surface for pial copyfile( subjects_dir / subject / "surf" / f"{hemi}.white", tmp_path / subject / "surf" / f"{hemi}.pial", ) copyfile( subjects_dir / subject / "surf" / f"{hemi}.curv", tmp_path / subject / "surf" / f"{hemi}.curv", ) copyfile( subjects_dir / subject / "surf" / f"{hemi}.inflated", tmp_path / subject / "surf" / f"{hemi}.inflated", ) if subject == "fsaverage": copyfile( subjects_dir / subject / "surf" / f"{hemi}.cortex.patch.flat", tmp_path / subject / "surf" / f"{hemi}.cortex.patch.flat", ) copyfile( subjects_dir / subject / "surf" / f"{hemi}.sphere", tmp_path / subject / "surf" / f"{hemi}.sphere", ) # now make realistic sEEG locations, picked from T1 raw.pick("eeg") raw.load_data() raw.set_eeg_reference([]) raw.set_channel_types({ch: "seeg" for ch in raw.ch_names}) pos = ( np.array( [ [25.85, 9.04, -5.38], [33.56, 9.04, -5.63], [40.44, 9.04, -5.06], [46.75, 9.04, -6.78], [-30.08, 9.04, 28.23], [-32.95, 9.04, 37.99], [-36.39, 9.04, 46.03], ] ) / 1000 ) raw.drop_channels(raw.ch_names[len(pos) :]) raw.set_montage( mne.channels.make_dig_montage( ch_pos=dict(zip(raw.ch_names, pos)), coord_frame="head" ) ) proj_info = _project_sensors_onto_inflated( raw.info, trans, "sample", subjects_dir=tmp_path ) assert_allclose( proj_info["chs"][0]["loc"][:3], np.array([0.0555809, 0.0034069, -0.04593032]), rtol=0.01, ) # check all on inflated surface x_dir = np.array([1.0, 0.0, 0.0]) head_mri_t = mne.transforms.invert_transform(trans) # need head->mri for hemi in ("lh", "rh"): coords, faces = mne.surface.read_surface( tmp_path / "sample" / "surf" / f"{hemi}.inflated" ) x_ = coords @ x_dir coords -= np.max(x_) * x_dir if hemi == "lh" else np.min(x_) * x_dir coords /= 1000 # mm -> m for ch in proj_info["chs"]: loc = ch["loc"][:3] if not np.isnan(loc).any() and (loc[0] <= 0) == (hemi == "lh"): assert ( np.linalg.norm( coords - mne.transforms.apply_trans(head_mri_t, loc), axis=1 ).min() < 1e-16 ) # test flat map montage = raw.get_montage() montage.apply_trans(mne.transforms.invert_transform(trans)) mri_mni_t = mne.read_talxfm("sample", subjects_dir) montage.apply_trans(mri_mni_t) # mri to mni_tal (MNI Taliarach) montage.apply_trans( mne.transforms.Transform(fro="mni_tal", to="mri", trans=np.eye(4)) ) raw.set_montage(montage) trans = mne.channels.compute_native_head_t(montage) flat_proj_info = _project_sensors_onto_inflated( raw.info, trans=trans, subject="fsaverage", subjects_dir=tmp_path, flat=True, ) # check all on flat surface x_dir = np.array([1.0, 0.0, 0.0]) head_mri_t = mne.transforms.invert_transform(trans) # need head->mri for hemi in ("lh", "rh"): coords, faces, _ = mne.surface._read_patch( tmp_path / "fsaverage" / "surf" / f"{hemi}.cortex.patch.flat" ) coords = coords[:, [1, 0, 2]] coords[:, 1] *= -1 x_ = coords @ x_dir coords -= np.max(x_) * x_dir if hemi == "lh" else np.min(x_) * x_dir coords /= 1000 # mm -> m for ch in flat_proj_info["chs"]: loc = ch["loc"][:3] if not np.isnan(loc).any() and (loc[0] <= 0) == (hemi == "lh"): assert ( np.linalg.norm( coords - mne.transforms.apply_trans(head_mri_t, loc), axis=1 ).min() < 1e-16 ) # plot to check # brain = mne.viz.Brain('fsaverage', subjects_dir=tempdir, alpha=0.5, # surf='flat') # brain.add_sensors(flat_proj_info, trans=trans)