Note

Go to the end to download the full example code.

Minimal example#

Below you can find an example script that contains all the ideas showcased in the Getting Started tutorial. It may serve as a good starting point for your own simulation.

import numpy as np
import mne

from mne.datasets import sample

from meegsim.coupling import ppc_von_mises
from meegsim.location import select_random
from meegsim.simulate import SourceSimulator
from meegsim.waveform import narrowband_oscillation

First, we need to load all the prerequisites for our simulation:

  • src - the mne.SourceSpaces object that describes all candidate source locations

  • fwd - the mne.Forward object that describes the forward model

  • info - the mne.Info object that describes the channel layout

In this simulation, we only use the EEG channels.

# Paths
data_path = sample.data_path() / "MEG" / "sample"
fwd_path = data_path / "sample_audvis-meg-eeg-oct-6-fwd.fif"
raw_path = data_path / "sample_audvis_raw.fif"

# Load the prerequisites: fwd, src, and info
fwd = mne.read_forward_solution(fwd_path)
fwd = mne.convert_forward_solution(fwd, force_fixed=True)
raw = mne.io.read_raw(raw_path)
src = fwd["src"]
info = raw.info

# Pick EEG channels only
eeg_idx = mne.pick_types(info, eeg=True)
info_eeg = mne.pick_info(info, eeg_idx)
fwd_eeg = fwd.pick_channels(info_eeg.ch_names)

Using default location ~/mne_data for sample...
Creating /home/docs/mne_data
Downloading file 'MNE-sample-data-processed.tar.gz' from 'https://osf.io/86qa2/download?version=6' to '/home/docs/mne_data'.

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Untarring contents of '/home/docs/mne_data/MNE-sample-data-processed.tar.gz' to '/home/docs/mne_data'
Attempting to create new mne-python configuration file:
/home/docs/.mne/mne-python.json
Could not read the /home/docs/.mne/mne-python.json json file during the writing. Assuming it is empty. Got: Expecting value: line 1 column 1 (char 0)
Download complete in 01m16s (1576.2 MB)
Reading forward solution from /home/docs/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif...
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    2 source spaces read
    Desired named matrix (kind = 3523 (FIFF_MNE_FORWARD_SOLUTION_GRAD)) not available
    Read MEG forward solution (7498 sources, 306 channels, free orientations)
    Desired named matrix (kind = 3523 (FIFF_MNE_FORWARD_SOLUTION_GRAD)) not available
    Read EEG forward solution (7498 sources, 60 channels, free orientations)
    Forward solutions combined: MEG, EEG
    Source spaces transformed to the forward solution coordinate frame
    Average patch normals will be employed in the rotation to the local surface coordinates....
    Converting to surface-based source orientations...
    [done]
Opening raw data file /home/docs/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.

Below we define the parameters of the simulation and the simulation itself. In this case, we place 50 noise (1/f) sources randomly and add two phase-coupled sources of alpha (8-12 Hz) activity in arbitrary locations:

# Simulation parameters
sfreq = 100  # in Hz
duration = 60  # in seconds

# Initialize
sim = SourceSimulator(src)

# Add 50 noise sources with random locations
sim.add_noise_sources(location=select_random, location_params=dict(n=50))

# Add two point sources with fixed locations, vertex indices are chosen
# arbitrarily to have one source in each hemisphere
lh_vertno = src[0]["vertno"][0]
rh_vertno = src[1]["vertno"][0]
sim.add_point_sources(
    location=[(0, lh_vertno), (1, rh_vertno)],
    waveform=narrowband_oscillation,
    waveform_params=dict(fmin=8, fmax=12),
    names=["s1", "s2"],
)

# Set the coupling between point sources
sim.set_coupling(
    ("s1", "s2"), method=ppc_von_mises, kappa=1, phase_lag=np.pi / 2, fmin=8, fmax=12
)

Now let’s simulate the configuration with a desired level of global SNR:

sc = sim.simulate(
    sfreq,
    duration,
    fwd=fwd_eeg,
    snr_global=3,
    snr_params=dict(fmin=8, fmax=12),
    random_state=0,
)

We can double-check that the defined sources were successfully added (for noise sources, we only print the total count):

print(f"Point sources: {sc._sources}")
print(f"The number of noise sources: {len(sc._noise_sources)}")

Point sources: {'s1': <PointSource | s1 | lh | 841>, 's2': <PointSource | s2 | rh | 1492>}
The number of noise sources: 50

Finally, we can obtain the simulated source activity and/or project it to sensor space while adding 1% of sensor noise:

stc = sc.to_stc()
raw = sc.to_raw(fwd_eeg, info_eeg, sensor_noise_level=0.01)

Projecting source estimate to sensor space...
[done]
Creating RawArray with float64 data, n_channels=59, n_times=6000
    Range : 0 ... 5999 =      0.000 ...    59.990 secs
Ready.

We can now plot the power spectra of simulated sensor-space data to verify that it has a mixture of 1/f and alpha activity as defined in the simulation:

spec = raw.compute_psd(
    method="welch", n_fft=2 * sfreq, n_overlap=sfreq, n_per_seg=2 * sfreq
)
spec.plot()
EEG
Effective window size : 2.000 (s)
Plotting power spectral density (dB=True).

<MNELineFigure size 1000x350 with 2 Axes>

Total running time of the script: (1 minutes 18.738 seconds)

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