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"""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)
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