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