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Compute a cross-spectral density (CSD) matrix#

A cross-spectral density (CSD) matrix is similar to a covariance matrix, but in the time-frequency domain. It is the first step towards computing sensor-to-sensor coherence or a DICS beamformer.

This script demonstrates the three methods that MNE-Python provides to compute the CSD:

  1. Using short-term Fourier transform: mne.time_frequency.csd_fourier()

  2. Using a multitaper approach: mne.time_frequency.csd_multitaper()

  3. Using Morlet wavelets: mne.time_frequency.csd_morlet()

# Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
# License: BSD-3-Clause

import mne
from mne.datasets import sample
from mne.time_frequency import csd_fourier, csd_multitaper, csd_morlet

print(__doc__)

In the following example, the computation of the CSD matrices can be performed using multiple cores. Set n_jobs to a value >1 to select the number of cores to use.

n_jobs = 1

Loading the sample dataset.

data_path = sample.data_path()
meg_path = data_path / "MEG" / "sample"
fname_raw = meg_path / "sample_audvis_raw.fif"
fname_event = meg_path / "sample_audvis_raw-eve.fif"
raw = mne.io.read_raw_fif(fname_raw)
events = mne.read_events(fname_event)

Opening raw data file /home/circleci/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.

By default, CSD matrices are computed using all MEG/EEG channels. When interpreting a CSD matrix with mixed sensor types, be aware that the measurement units, and thus the scalings, differ across sensors. In this example, for speed and clarity, we select a single channel type: gradiometers.

picks = mne.pick_types(raw.info, meg="grad")

# Make some epochs, based on events with trigger code 1
epochs = mne.Epochs(
    raw,
    events,
    event_id=1,
    tmin=-0.2,
    tmax=1,
    picks=picks,
    baseline=(None, 0),
    reject=dict(grad=4000e-13),
    preload=True,
)

Not setting metadata
72 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] s
Applying baseline correction (mode: mean)
3 projection items activated
Loading data for 72 events and 722 original time points ...
0 bad epochs dropped

Computing CSD matrices using short-term Fourier transform and (adaptive) multitapers is straightforward:

csd_fft = csd_fourier(epochs, fmin=15, fmax=20, n_jobs=n_jobs)
csd_mt = csd_multitaper(epochs, fmin=15, fmax=20, adaptive=True, n_jobs=n_jobs)

Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Computing cross-spectral density from epochs...

  0%|          | CSD epoch blocks : 0/72 [00:00<?,       ?it/s]
  1%|1         | CSD epoch blocks : 1/72 [00:00<00:03,   19.78it/s]
  3%|2         | CSD epoch blocks : 2/72 [00:00<00:02,   29.18it/s]
  4%|4         | CSD epoch blocks : 3/72 [00:00<00:01,   35.69it/s]
  6%|5         | CSD epoch blocks : 4/72 [00:00<00:01,   37.18it/s]
  7%|6         | CSD epoch blocks : 5/72 [00:00<00:01,   38.20it/s]
  8%|8         | CSD epoch blocks : 6/72 [00:00<00:01,   39.55it/s]
 10%|9         | CSD epoch blocks : 7/72 [00:00<00:01,   41.94it/s]
 11%|#1        | CSD epoch blocks : 8/72 [00:00<00:01,   43.64it/s]
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 19%|#9        | CSD epoch blocks : 14/72 [00:00<00:01,   46.77it/s]
 21%|##        | CSD epoch blocks : 15/72 [00:00<00:01,   47.65it/s]
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 50%|#####     | CSD epoch blocks : 36/72 [00:00<00:00,   56.40it/s]
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 81%|########  | CSD epoch blocks : 58/72 [00:01<00:00,   57.81it/s]
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 90%|######### | CSD epoch blocks : 65/72 [00:01<00:00,   57.02it/s]
 92%|#########1| CSD epoch blocks : 66/72 [00:01<00:00,   56.23it/s]
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 99%|#########8| CSD epoch blocks : 71/72 [00:01<00:00,   55.55it/s]
100%|##########| CSD epoch blocks : 72/72 [00:01<00:00,   55.49it/s]
100%|##########| CSD epoch blocks : 72/72 [00:01<00:00,   54.71it/s]
[done]
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
    Using multitaper spectrum estimation with 7 DPSS windows
Computing cross-spectral density from epochs...

  0%|          | CSD epoch blocks : 0/72 [00:00<?,       ?it/s]
  1%|1         | CSD epoch blocks : 1/72 [00:00<00:16,    4.34it/s]
  3%|2         | CSD epoch blocks : 2/72 [00:00<00:13,    5.33it/s]
  4%|4         | CSD epoch blocks : 3/72 [00:00<00:12,    5.70it/s]
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 19%|#9        | CSD epoch blocks : 14/72 [00:02<00:09,    6.21it/s]
 21%|##        | CSD epoch blocks : 15/72 [00:02<00:09,    6.12it/s]
 22%|##2       | CSD epoch blocks : 16/72 [00:02<00:09,    6.18it/s]
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 71%|#######   | CSD epoch blocks : 51/72 [00:07<00:03,    6.59it/s]
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 81%|########  | CSD epoch blocks : 58/72 [00:08<00:02,    6.67it/s]
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 83%|########3 | CSD epoch blocks : 60/72 [00:09<00:01,    6.69it/s]
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 88%|########7 | CSD epoch blocks : 63/72 [00:09<00:01,    6.72it/s]
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 90%|######### | CSD epoch blocks : 65/72 [00:09<00:01,    6.74it/s]
 92%|#########1| CSD epoch blocks : 66/72 [00:10<00:00,    6.75it/s]
 93%|#########3| CSD epoch blocks : 67/72 [00:10<00:00,    6.75it/s]
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 96%|#########5| CSD epoch blocks : 69/72 [00:10<00:00,    6.77it/s]
 97%|#########7| CSD epoch blocks : 70/72 [00:10<00:00,    6.78it/s]
 99%|#########8| CSD epoch blocks : 71/72 [00:10<00:00,    6.78it/s]
100%|##########| CSD epoch blocks : 72/72 [00:10<00:00,    6.79it/s]
100%|##########| CSD epoch blocks : 72/72 [00:10<00:00,    6.56it/s]
[done]

When computing the CSD with Morlet wavelets, you specify the exact frequencies at which to compute it. For each frequency, a corresponding wavelet will be constructed and convolved with the signal, resulting in a time-frequency decomposition.

The CSD is constructed by computing the correlation between the time-frequency representations between all sensor-to-sensor pairs. The time-frequency decomposition originally has the same sampling rate as the signal, in our case ~600Hz. This means the decomposition is over-specified in time and we may not need to use all samples during our CSD computation, just enough to get a reliable correlation statistic. By specifying decim=10, we use every 10th sample, which will greatly speed up the computation and will have a minimal effect on the CSD.

frequencies = [16, 17, 18, 19, 20]
csd_wav = csd_morlet(epochs, frequencies, decim=10, n_jobs=n_jobs)

Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Computing cross-spectral density from epochs...

  0%|          | CSD epoch blocks : 0/72 [00:00<?,       ?it/s]
  1%|1         | CSD epoch blocks : 1/72 [00:00<00:06,   10.33it/s]
  3%|2         | CSD epoch blocks : 2/72 [00:00<00:05,   12.16it/s]
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 21%|##        | CSD epoch blocks : 15/72 [00:01<00:04,   14.14it/s]
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 85%|########4 | CSD epoch blocks : 61/72 [00:04<00:00,   14.58it/s]
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100%|##########| CSD epoch blocks : 72/72 [00:04<00:00,   14.46it/s]
[done]

The resulting mne.time_frequency.CrossSpectralDensity objects have a plotting function we can use to compare the results of the different methods. We’re plotting the mean CSD across frequencies. mne.time_frequency.CrossSpectralDensity.plot() returns a list of created figures; in this case, each returned list has only one figure so we use a Python trick of including a comma after our variable name to assign the figure (not the list) to our fig variable:

plot_dict = {
    "Short-time Fourier transform": csd_fft,
    "Adaptive multitapers": csd_mt,
    "Morlet wavelet transform": csd_wav,
}
for title, csd in plot_dict.items():
    (fig,) = csd.mean().plot()
    fig.suptitle(title)
  • Short-time Fourier transform, 15.8-20.0 Hz.
  • Adaptive multitapers, 15.8-20.0 Hz.
  • Morlet wavelet transform, 16.0-20.0 Hz.

Total running time of the script: ( 0 minutes 22.672 seconds)

Estimated memory usage: 133 MB

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