Note

Click here to download the full example code

# 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 this dataset: Somatosensory

## 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

```
# 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:

```
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)
# 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 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)
```

Out:

```
Opening raw data file /home/circleci/mne_data/MNE-somato-data/sub-01/meg/sub-01_task-somato_meg.fif...
Range : 237600 ... 506999 = 791.189 ... 1688.266 secs
Ready.
Current compensation grade : 0
111 events found
Event IDs: [1]
111 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
0 projection items activated
Loading data for 111 events and 1052 original time points ...
0 bad epochs dropped
Reading forward solution from /home/circleci/mne_data/MNE-somato-data/derivatives/sub-01/sub-01_task-somato-fwd.fif...
Reading a source space...
[done]
Reading a source space...
[done]
2 source spaces read
Desired named matrix (kind = 3523) not available
Read MEG forward solution (8155 sources, 306 channels, free orientations)
Source spaces transformed to the forward solution coordinate frame
```

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

```
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.

```
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)
```

Out:

```
Computing cross-spectral density from epochs...
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[done]
```

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

Out:

```
Computing inverse operator with 204 channels.
204 out of 306 channels remain after picking
Selected 204 channels
Creating the depth weighting matrix...
Whitening the forward solution.
Computing data rank from covariance with rank=None
Using tolerance 4.5e+08 (2.2e-16 eps * 204 dim * 1e+22 max singular value)
Estimated rank (grad): 204
GRAD: rank 204 computed from 204 data channels with 0 projectors
Setting small GRAD eigenvalues to zero (without PCA)
Creating the source covariance matrix
Adjusting source covariance matrix.
Computing DICS spatial filters...
Computing beamformer filters for 8155 sources
Filter computation complete
```

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

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

Out:

```
Computing DICS source power...
[done]
Computing DICS source power...
[done]
```

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

```
stc = beta_source_power / baseline_source_power
stc.subject = '01' # it's mis-coded in fwd['src']
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)
```

Out:

```
Using control points [1.49740671 1.57775391 1.85215653]
```

**Total running time of the script:** ( 0 minutes 41.639 seconds)

**Estimated memory usage:** 425 MB