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%matplotlib inline

Compute source power using DICS beamformer

Compute a Dynamic Imaging of Coherent Sources (DICS) :footcite:GrossEtAl2001 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 this dataset: somato-dataset



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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>
#         Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD (3-clause)
import os.path as op

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()
subject = '01'
task = 'somato'
raw_fname = op.join(data_path, 'sub-{}'.format(subject), 'meg',
                    'sub-{}_task-{}_meg.fif'.format(subject, task))

raw = mne.io.read_raw_fif(raw_fname)

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

# Read forward operator and point to freesurfer subject directory
fname_fwd = op.join(data_path, 'derivatives', 'sub-{}'.format(subject),
                    'sub-{}_task-{}-fwd.fif'.format(subject, task))
subjects_dir = op.join(data_path, 'derivatives', 'freesurfer', 'subjects')

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)

To compute the source power for a frequency band, rather than each frequency separately, we average the CSD objects across frequencies.



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csd = csd.mean()
csd_baseline = csd_baseline.mean()
csd_ers = csd_ers.mean()

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, noise_csd=csd_baseline,
                    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, filters)
beta_source_power, freqs = apply_dics_csd(csd_ers, 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,
                 subject=subject, time_label=message)

References

.. footbibliography::