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%matplotlib inline

Compute source power using DICS beamfomer

Compute a Dynamic Imaging of Coherent Sources (DICS) [1]_ filter from single-trial activity to estimate source power across a frequency band. This example demonstrates how to source localize the event-related synchronization (ERS) of beta band activity in the "somato" dataset.

References

.. [1] Gross et al. Dynamic imaging of coherent sources: Studying neural interactions in the human brain. PNAS (2001) vol. 98 (2) pp. 694-699



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# Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
#         Roman Goj <roman.goj@gmail.com>
#         Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import somato
from mne.time_frequency import csd_morlet
from mne.beamformer import make_dics, apply_dics_csd

print(__doc__)

Reading the raw data and creating epochs:



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data_path = somato.data_path()
raw_fname = data_path + '/MEG/somato/sef_raw_sss.fif'
fname_fwd = data_path + '/MEG/somato/somato-meg-oct-6-fwd.fif'
subjects_dir = data_path + '/subjects'

raw = mne.io.read_raw_fif(raw_fname)

# Set picks, use a single sensor type
picks = mne.pick_types(raw.info, meg='grad', exclude='bads')

# Read epochs
events = mne.find_events(raw)
epochs = mne.Epochs(raw, events, event_id=1, tmin=-1.5, tmax=2, picks=picks,
                    preload=True)

# Read forward operator
fwd = mne.read_forward_solution(fname_fwd)

We are interested in the beta band. Define a range of frequencies, using a log scale, from 12 to 30 Hz.



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freqs = np.logspace(np.log10(12), np.log10(30), 9)

Computing the cross-spectral density matrix for the beta frequency band, for different time intervals. We use a decim value of 20 to speed up the computation in this example at the loss of accuracy.



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csd = csd_morlet(epochs, freqs, tmin=-1, tmax=1.5, decim=20)
csd_baseline = csd_morlet(epochs, freqs, tmin=-1, tmax=0, decim=20)
# ERS activity starts at 0.5 seconds after stimulus onset
csd_ers = csd_morlet(epochs, freqs, tmin=0.5, tmax=1.5, decim=20)

Computing DICS spatial filters using the CSD that was computed on the entire timecourse.



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filters = make_dics(epochs.info, fwd, csd.mean(), pick_ori='max-power')

Applying DICS spatial filters separately to the CSD computed using the baseline and the CSD computed during the ERS activity.



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baseline_source_power, freqs = apply_dics_csd(csd_baseline.mean(), filters)
beta_source_power, freqs = apply_dics_csd(csd_ers.mean(), filters)

Visualizing source power during ERS activity relative to the baseline power.



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stc = beta_source_power / baseline_source_power
message = 'DICS source power in the 12-30 Hz frequency band'
brain = stc.plot(hemi='both', views='par', subjects_dir=subjects_dir,
                 time_label=message)