Compute induced power in the source space with dSPMΒΆ

Returns STC files ie source estimates of induced power for different bands in the source space. The inverse method is linear based on dSPM inverse operator.

# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)

import matplotlib.pyplot as plt

import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, source_band_induced_power

print(__doc__)

Set parameters

data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
tmin, tmax, event_id = -0.2, 0.5, 1

# Setup for reading the raw data
raw = io.Raw(raw_fname)
events = mne.find_events(raw, stim_channel='STI 014')
inverse_operator = read_inverse_operator(fname_inv)

include = []
raw.info['bads'] += ['MEG 2443', 'EEG 053']  # bads + 2 more

# picks MEG gradiometers
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,
                       stim=False, include=include, exclude='bads')

# Load condition 1
event_id = 1
events = events[:10]  # take 10 events to keep the computation time low
# Use linear detrend to reduce any edge artifacts
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6),
                    preload=True, detrend=1)

# Compute a source estimate per frequency band
bands = dict(alpha=[9, 11], beta=[18, 22])

stcs = source_band_induced_power(epochs, inverse_operator, bands, n_cycles=2,
                                 use_fft=False, n_jobs=1)

for b, stc in stcs.iteritems():
    stc.save('induced_power_%s' % b)

plot mean power

plt.plot(stcs['alpha'].times, stcs['alpha'].data.mean(axis=0), label='Alpha')
plt.plot(stcs['beta'].times, stcs['beta'].data.mean(axis=0), label='Beta')
plt.xlabel('Time (ms)')
plt.ylabel('Power')
plt.legend()
plt.title('Mean source induced power')
plt.show()
../../_images/sphx_glr_plot_source_space_time_frequency_001.png

Total running time of the script: (0 minutes 7.780 seconds)

Download Python source code: plot_source_space_time_frequency.py