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%matplotlib inline
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# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, compute_source_psd_epochs
print(__doc__)
data_path = sample.data_path()
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
fname_raw = data_path + '/MEG/sample/sample_audvis_raw.fif'
fname_event = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'
label_name = 'Aud-lh'
fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name
subjects_dir = data_path + '/subjects'
event_id, tmin, tmax = 1, -0.2, 0.5
snr = 1.0 # use smaller SNR for raw data
lambda2 = 1.0 / snr ** 2
method = "dSPM" # use dSPM method (could also be MNE or sLORETA)
# Load data
inverse_operator = read_inverse_operator(fname_inv)
label = mne.read_label(fname_label)
raw = mne.io.read_raw_fif(fname_raw)
events = mne.read_events(fname_event)
# Set up pick list
include = []
raw.info['bads'] += ['EEG 053'] # bads + 1 more
# pick MEG channels
picks = mne.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))
# define frequencies of interest
fmin, fmax = 0., 70.
bandwidth = 4. # bandwidth of the windows in Hz
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n_epochs_use = 10
stcs = compute_source_psd_epochs(epochs[:n_epochs_use], inverse_operator,
lambda2=lambda2,
method=method, fmin=fmin, fmax=fmax,
bandwidth=bandwidth, label=label,
return_generator=True, verbose=True)
# compute average PSD over the first 10 epochs
psd_avg = 0.
for i, stc in enumerate(stcs):
psd_avg += stc.data
psd_avg /= n_epochs_use
freqs = stc.times # the frequencies are stored here
stc.data = psd_avg # overwrite the last epoch's data with the average
Visualize the 10 Hz PSD:
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brain = stc.plot(initial_time=10., hemi='lh', views='lat', # 10 HZ
clim=dict(kind='value', lims=(20, 40, 60)),
smoothing_steps=3, subjects_dir=subjects_dir)
brain.add_label(label, borders=True, color='k')
Visualize the entire spectrum:
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fig, ax = plt.subplots()
ax.plot(freqs, psd_avg.mean(axis=0))
ax.set_xlabel('Freq (Hz)')
ax.set_xlim(stc.times[[0, -1]])
ax.set_ylabel('Power Spectral Density')