picks=picks,
baseline=(None, 0),
reject=dict(mag=4e-12, grad=4000e-13, eog=150e-6))
# Compute inverse solution and stcs for each epoch
stcs = apply_inverse_epochs(epochs,
inverse_operator,
lambda2,
method,
label,
pick_normal=True)
data = sum(stc.data for stc in stcs) / len(stcs)
# compute sign flip to avoid signal cancelation when averaging signed values
flip = mne.label_sign_flip(label, inverse_operator['src'])
label_mean = np.mean(data, axis=0)
label_mean_flip = np.mean(flip[:, np.newaxis] * data, axis=0)
###############################################################################
# View activation time-series
pl.figure()
h0 = pl.plot(1e3 * stcs[0].times, data.T, 'k')
h1 = pl.plot(1e3 * stcs[0].times, label_mean, 'r', linewidth=3)
h2 = pl.plot(1e3 * stcs[0].times, label_mean_flip, 'g', linewidth=3)
pl.legend((h0[0], h1, h2),
('all dipoles in label', 'mean', 'mean with sign flip'))
pl.xlabel('time (ms)')
pl.ylabel('dSPM value')
pl.show()
evoked = epochs.average() # Compute inverse solution and stcs for each epoch # Use the same inverse operator as with evoked data (i.e., set nave) # If you use a different nave, dSPM just scales by a factor sqrt(nave) stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method, label, pick_ori="normal", nave=evoked.nave) stc_evoked = apply_inverse(evoked, inverse_operator, lambda2, method, pick_ori="normal") stc_evoked_label = stc_evoked.in_label(label) # Mean across trials but not across vertices in label mean_stc = sum(stcs) / len(stcs) # compute sign flip to avoid signal cancelation when averaging signed values flip = mne.label_sign_flip(label, inverse_operator["src"]) label_mean = np.mean(mean_stc.data, axis=0) label_mean_flip = np.mean(flip[:, np.newaxis] * mean_stc.data, axis=0) # Get inverse solution by inverting evoked data stc_evoked = apply_inverse(evoked, inverse_operator, lambda2, method, pick_ori="normal") # apply_inverse() does whole brain, so sub-select label of interest stc_evoked_label = stc_evoked.in_label(label) # Average over label (not caring to align polarities here) label_mean_evoked = np.mean(stc_evoked_label.data, axis=0) ############################################################################### # View activation time-series to illustrate the benefit of aligning/flipping
# pick MEG channels
picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,
include=include, exclude='bads')
# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(mag=4e-12, grad=4000e-13,
eog=150e-6))
# Compute inverse solution and stcs for each epoch
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method, label,
pick_ori="normal")
mean_stc = sum(stcs) / len(stcs)
# compute sign flip to avoid signal cancelation when averaging signed values
flip = mne.label_sign_flip(label, inverse_operator['src'])
label_mean = np.mean(mean_stc.data, axis=0)
label_mean_flip = np.mean(flip[:, np.newaxis] * mean_stc.data, axis=0)
###############################################################################
# View activation time-series
plt.figure()
h0 = plt.plot(1e3 * stcs[0].times, mean_stc.data.T, 'k')
h1, = plt.plot(1e3 * stcs[0].times, label_mean, 'r', linewidth=3)
h2, = plt.plot(1e3 * stcs[0].times, label_mean_flip, 'g', linewidth=3)
plt.legend((h0[0], h1, h2), ('all dipoles in label', 'mean',
'mean with sign flip'))
plt.xlabel('time (ms)')
plt.ylabel('dSPM value')
plt.show()
def Label_source_flip(condnames,ListSubj,modality,Method,covmatsource,parc):
condnames = ('Qt_all','Qs_all')
ListSubj = (
'sd130343', 'cb130477', 'rb130313',
'jm100109', 'sb120316', 'tk130502',
'lm130479', 'ms130534', 'ma100253',
'sl130503', 'mb140004', 'mp140019',
'dm130250', 'hr130504', 'wl130316',
'rl130571')
modality = 'MEG'
Method = 'dSPM'
covmatsource = 'QT'
parc = 'aparc'
import mne
import numpy as np
from mne.minimum_norm import apply_inverse, make_inverse_operator
from mne.minimum_norm import apply_inverse_epochs
import os
from matplotlib import pyplot as plt
os.chdir('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/SCRIPTS/MNE_PYTHON')
os.environ['SUBJECTS_DIR'] = '/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/mri'
os.environ['MNE_ROOT'] = '/neurospin/local/mne'
wdir = "/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/"
for c in range(len(condnames)):
label_mean,label_mean_flip,label_mean_ev = [],[],[]
for i in range(len(ListSubj)):
subjects_dir = '/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/mri/'
labels = mne.read_labels_from_annot('fsaverage',
parc,
hemi = 'both',
subjects_dir = ListSubj[i])
label = labels[6]
# which modality?
if modality == 'MEG':
megtag=True
eegtag=False
fname_fwd = (wdir+ListSubj[i]+"/mne_python/run3_ico-5_megonly_-fwd.fif")
elif modality == 'EEG':
megtag=False
eegtag=True
fname_fwd = (wdir+ListSubj[i]+"/mne_python/run3_ico-5_eegonly_-fwd.fif")
elif modality == 'MEEG':
megtag=True
eegtag=True
fname_fwd = (wdir+ListSubj[i]+"/mne_python/run3_ico-5_meeg_-fwd.fif")
# load noise covariance matrice
fname_noisecov = (wdir+ListSubj[i]+"/mne_python/COVMATS/"+modality
+"_noisecov_"+ covmatsource +"_"+ ListSubj[i] +"-cov.fif")
NOISE_COV1 = mne.read_cov(fname_noisecov)
# load MEEG epochs, then pick
fname_epochs = (wdir+ListSubj[i]+"/mne_python/EPOCHS/MEEG_epochs_"
+condnames[c]+ '_' + ListSubj[i]+"-epo.fif")
epochs = mne.read_epochs(fname_epochs)
epochs.pick_types(meg=megtag, eeg=eegtag, stim=False , eog=False)
# compute evoked
evokedcond1 = epochs.average()
forward = mne.read_forward_solution(fname_fwd ,surf_ori=True)
inverse_operator1 = make_inverse_operator(evokedcond1.info, forward,
NOISE_COV1, loose=0.2, depth=0.8)
snr = 3.0
lambda2 = 1.0 / snr **2
# MEG source reconstruction
stc,stc_ev, label_mean_evoked = [], [], []
stc= apply_inverse_epochs(epochs, inverse_operator1, lambda2,
method =Method, label=label, pick_ori= "normal",nave=evokedcond1.nave)
stc_ev= apply_inverse(evokedcond1, inverse_operator1, lambda2,
method =Method, label=label, pick_ori= "normal")
label_mean_evoked = np.mean(stc_ev.data, axis=0)
# Mean across trials but not across vertices in label
mean_stc = []
mean_stc = sum(stc) / len(stc)
# compute sign flip to avoid signal cancelation when averaging signed values
flip = mne.label_sign_flip(label, inverse_operator1['src'])
label_mean.append(np.mean(mean_stc.data, axis=0))
label_mean_flip.append(np.mean(flip[:, np.newaxis] * mean_stc.data, axis=0))
label_mean_ev.append(label_mean_evoked)
LabMean = np.mean(np.array(label_mean),axis=0)
LabMeanFlip = np.mean(np.array(label_mean_flip),axis=0)
LabelmeanEv = np.mean(np.array(label_mean_ev),axis=0)
############################## PLOT ###############################
times = 1e3 * stc[0].times # times in ms
plt.figure()
h1, = plt.plot(times, LabMean, 'r', linewidth=3)
h2, = plt.plot(times, LabMeanFlip, 'g', linewidth=3)
h3, = plt.plot(times, LabelmeanEv, 'k', linewidth=3)
plt.legend((h1, h2, h3), ('mean',
'mean with sign flip', 'evoked'))
plt.xlabel('time (ms)')
plt.ylabel('dSPM value')
plt.show()
plt.figure()
plt.plot(times,np.transpose(np.array(label_mean),[1, 0]))
plt.figure()
plt.plot(times,np.transpose(np.array(label_mean_flip),[1, 0]))
plt.figure()
plt.plot(np.transpose(mean_stc.data,[1,0]))
plt.figure()
plt.plot(np.transpose(flip[:, np.newaxis] * mean_stc.data,[1,0]))
plt.figure()
plt.plot(np.mean(mean_stc.data, axis=0))
plt.figure()
plt.plot(np.mean(flip[:, np.newaxis] * mean_stc.data, axis=0))