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# Author: John G Samuelsson <johnsam@mit.edu>
import numpy as np
from mne.preprocessing._css import cortical_signal_suppression
from mne import pick_types, read_evokeds
from mne.datasets import testing
data_path = testing.data_path(download=False)
fname_evoked = data_path / 'MEG' / 'sample' / 'sample_audvis-ave.fif'
@testing.requires_testing_data
def test_cortical_signal_suppression():
"""Test that CSS is dampening the cortical signal and has right shape."""
ave = read_evokeds(fname_evoked)[0]
eeg_ind = pick_types(ave.info, eeg=True)
mag_ind = pick_types(ave.info, meg='mag')
grad_ind = pick_types(ave.info, meg='grad')
ave.data[mag_ind][0, :] = np.sin(2 * np.pi * 40 * ave.times) * \
np.mean(np.abs(ave.data[mag_ind][0, :]))
ave.data[mag_ind][1, :] = np.sin(2 * np.pi * 239 * ave.times) * \
np.mean(np.abs(ave.data[mag_ind][1, :]))
ave.data[grad_ind][0, :] = np.sin(2 * np.pi * 40 * ave.times) * \
np.mean(np.abs(ave.data[grad_ind][0, :]))
ave.data[eeg_ind][0, :] = np.sin(2 * np.pi * 40 * ave.times) * \
np.mean(np.abs(ave.data[eeg_ind][0, :]))
ave.data[eeg_ind][1, :] = np.sin(2 * np.pi * 239 * ave.times) * \
np.mean(np.abs(ave.data[eeg_ind][1, :]))
ave_f = cortical_signal_suppression(ave)
cort_power = np.sum(np.abs(ave.data[eeg_ind][0, :]))
deep_power = np.sum(np.abs(ave.data[eeg_ind][1, :]))
cort_power_f = np.sum(np.abs(ave_f.data[eeg_ind][0, :]))
deep_power_f = np.sum(np.abs(ave_f.data[eeg_ind][1, :]))
rel_SNR_gain = (deep_power_f / deep_power) / (cort_power_f / cort_power)
assert rel_SNR_gain > 0
assert ave_f.data.shape == ave.data.shape
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