In [ ]:
%matplotlib inline
In [ ]:
import numpy as np
import matplotlib.pyplot as pl
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse_epochs
import mne
from jumeg.jumeg_surrogates import Surrogates, check_power_spectrum
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
fname_raw = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
fname_event = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
stcs_testing = True
raw = mne.io.read_raw_fif(fname_raw)
events = mne.read_events(fname_event)
inverse_operator = read_inverse_operator(fname_inv)
# add a bad channel
raw.info['bads'] += ['MEG 2443']
# pick MEG channels
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False,
exclude='bads')
# define epochs for left-auditory condition
event_id, tmin, tmax = 1, -0.2, 0.5
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(mag=4e-12, grad=4000e-13))
snr = 1.0 # use lower SNR for single epochs
lambda2 = 1.0 / snr ** 2
method = "dSPM" # use dSPM method (could also be MNE or sLORETA)
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method,
pick_ori="normal", return_generator=False)
# compute surrogates for one STC
surr_stcs = Surrogates(stcs[0])
mysurr = surr_stcs.compute_surrogates(n_surr=10, return_generator=False)
# check if surrogates are correctly computed
assert not np.array_equal(mysurr[0].data,
mysurr[1].data), 'Surrogates mostly equal'
assert not np.array_equal(stcs[0].data,
mysurr[2].data), 'Surrogates equal to original'
# visualize results by plotting 1 the average across voxels
# of original and surrogate STC
pl.plot(stcs[0].data.mean(axis=0))
for i in mysurr:
pl.plot(i.data.mean(axis=0), color='r')
pl.title('Averaged real vs surrogate source time courses')
pl.show()