def test_eeg_field_interpolation():
"""Test interpolation of EEG field onto head
"""
trans = read_trans(trans_fname)
info = read_info(evoked_fname)
surf = get_head_surface('sample', subjects_dir=subjects_dir)
# we must have trans if surface is in MRI coords
assert_raises(ValueError, make_surface_mapping, info, surf, 'eeg')
data = make_surface_mapping(info, surf, 'eeg', trans, mode='accurate')
assert_array_equal(data.shape, (2562, 60)) # maps data onto surf
def test_head():
"""Test loading the head surface
"""
surf_1 = get_head_surface('sample', subjects_dir=subjects_dir)
surf_2 = get_head_surface('sample', 'head', subjects_dir=subjects_dir)
assert_true(len(surf_1['rr']) < len(surf_2['rr'])) # BEM vs dense head
import numpy as np
import mne
data_path = mne.datasets.sample.data_path()
subjects_dir = data_path + '/subjects'
evoked_fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
trans_fname = data_path + '/MEG/sample/sample_audvis_raw-trans.fif'
setno = 'Left Auditory'
trans = mne.read_trans(trans_fname)
evoked = mne.fiff.read_evoked(evoked_fname, setno=setno,
baseline=(-0.2, 0.0))
# let's do this in MRI coordinates so they're easy to plot
helmet_surf = mne.get_meg_helmet_surf(evoked.info, trans)
head_surf = mne.get_head_surface('sample', subjects_dir=subjects_dir)
helmet_map = mne.make_surface_mapping(evoked.info, helmet_surf, 'meg', trans,
n_jobs=-1)
head_map = mne.make_surface_mapping(evoked.info, head_surf, 'eeg', trans,
n_jobs=-1)
# let's look at the N100
evoked.crop(0.09, 0.10)
evoked_eeg = mne.fiff.pick_types_evoked(evoked, meg=False, eeg=True)
evoked_meg = mne.fiff.pick_types_evoked(evoked, meg=True, eeg=False)
helmet_data = np.dot(helmet_map, evoked_meg.data[:, 0])
head_data = np.dot(head_map, evoked_eeg.data[:, 0])
# Plot them
from mayavi import mlab
