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:

Using short-term Fourier transform: mne.time_frequency.csd_fourier()
Using a multitaper approach: mne.time_frequency.csd_multitaper()
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] sec
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,   21.30it/s]
  3%|2         | CSD epoch blocks : 2/72 [00:00<00:02,   28.23it/s]
  4%|4         | CSD epoch blocks : 3/72 [00:00<00:02,   32.92it/s]
  6%|5         | CSD epoch blocks : 4/72 [00:00<00:01,   36.77it/s]
  7%|6         | CSD epoch blocks : 5/72 [00:00<00:01,   39.57it/s]
  8%|8         | CSD epoch blocks : 6/72 [00:00<00:01,   41.72it/s]
 10%|9         | CSD epoch blocks : 7/72 [00:00<00:01,   43.42it/s]
 11%|#1        | CSD epoch blocks : 8/72 [00:00<00:01,   44.78it/s]
 12%|#2        | CSD epoch blocks : 9/72 [00:00<00:01,   45.90it/s]
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 15%|#5        | CSD epoch blocks : 11/72 [00:00<00:01,   47.54it/s]
 17%|#6        | CSD epoch blocks : 12/72 [00:00<00:01,   48.27it/s]
 18%|#8        | CSD epoch blocks : 13/72 [00:00<00:01,   48.81it/s]
 19%|#9        | CSD epoch blocks : 14/72 [00:00<00:01,   49.13it/s]
 21%|##        | CSD epoch blocks : 15/72 [00:00<00:01,   49.23it/s]
 22%|##2       | CSD epoch blocks : 16/72 [00:00<00:01,   49.57it/s]
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 50%|#####     | CSD epoch blocks : 36/72 [00:00<00:00,   52.78it/s]
 51%|#####1    | CSD epoch blocks : 37/72 [00:00<00:00,   52.97it/s]
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 79%|#######9  | CSD epoch blocks : 57/72 [00:01<00:00,   53.23it/s]
 81%|########  | CSD epoch blocks : 58/72 [00:01<00:00,   53.27it/s]
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 90%|######### | CSD epoch blocks : 65/72 [00:01<00:00,   52.78it/s]
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100%|##########| CSD epoch blocks : 72/72 [00:01<00:00,   53.29it/s]
100%|##########| CSD epoch blocks : 72/72 [00:01<00:00,   52.12it/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,    4.30it/s]
  3%|2         | CSD epoch blocks : 2/72 [00:00<00:12,    5.53it/s]
  4%|4         | CSD epoch blocks : 3/72 [00:00<00:11,    6.14it/s]
  6%|5         | CSD epoch blocks : 4/72 [00:00<00:10,    6.48it/s]
  7%|6         | CSD epoch blocks : 5/72 [00:00<00:09,    6.71it/s]
  8%|8         | CSD epoch blocks : 6/72 [00:00<00:09,    6.88it/s]
 10%|9         | CSD epoch blocks : 7/72 [00:01<00:09,    6.99it/s]
 11%|#1        | CSD epoch blocks : 8/72 [00:01<00:09,    6.81it/s]
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 15%|#5        | CSD epoch blocks : 11/72 [00:01<00:09,    6.71it/s]
 17%|#6        | CSD epoch blocks : 12/72 [00:01<00:08,    6.80it/s]
 18%|#8        | CSD epoch blocks : 13/72 [00:01<00:08,    6.88it/s]
 19%|#9        | CSD epoch blocks : 14/72 [00:02<00:08,    6.96it/s]
 21%|##        | CSD epoch blocks : 15/72 [00:02<00:08,    7.02it/s]
 22%|##2       | CSD epoch blocks : 16/72 [00:02<00:07,    7.09it/s]
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 31%|###       | CSD epoch blocks : 22/72 [00:03<00:06,    7.16it/s]
 32%|###1      | CSD epoch blocks : 23/72 [00:03<00:06,    7.14it/s]
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 50%|#####     | CSD epoch blocks : 36/72 [00:05<00:04,    7.35it/s]
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 71%|#######   | CSD epoch blocks : 51/72 [00:06<00:02,    7.57it/s]
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 81%|########  | CSD epoch blocks : 58/72 [00:07<00:01,    7.62it/s]
 82%|########1 | CSD epoch blocks : 59/72 [00:07<00:01,    7.63it/s]
 83%|########3 | CSD epoch blocks : 60/72 [00:08<00:01,    7.64it/s]
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 86%|########6 | CSD epoch blocks : 62/72 [00:08<00:01,    7.64it/s]
 88%|########7 | CSD epoch blocks : 63/72 [00:08<00:01,    7.65it/s]
 89%|########8 | CSD epoch blocks : 64/72 [00:08<00:01,    7.66it/s]
 90%|######### | CSD epoch blocks : 65/72 [00:08<00:00,    7.66it/s]
 92%|#########1| CSD epoch blocks : 66/72 [00:08<00:00,    7.67it/s]
 93%|#########3| CSD epoch blocks : 67/72 [00:09<00:00,    7.67it/s]
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 96%|#########5| CSD epoch blocks : 69/72 [00:09<00:00,    7.67it/s]
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 99%|#########8| CSD epoch blocks : 71/72 [00:09<00:00,    7.62it/s]
100%|##########| CSD epoch blocks : 72/72 [00:09<00:00,    7.63it/s]
100%|##########| CSD epoch blocks : 72/72 [00:09<00:00,    7.43it/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<00:08,    8.36it/s]
  3%|2         | CSD epoch blocks : 2/72 [00:00<00:06,   10.07it/s]
  4%|4         | CSD epoch blocks : 3/72 [00:00<00:06,   10.96it/s]
  6%|5         | CSD epoch blocks : 4/72 [00:00<00:05,   11.48it/s]
  7%|6         | CSD epoch blocks : 5/72 [00:00<00:05,   11.78it/s]
  8%|8         | CSD epoch blocks : 6/72 [00:00<00:05,   11.94it/s]
 10%|9         | CSD epoch blocks : 7/72 [00:00<00:05,   12.05it/s]
 11%|#1        | CSD epoch blocks : 8/72 [00:00<00:05,   12.06it/s]
 12%|#2        | CSD epoch blocks : 9/72 [00:00<00:05,   12.09it/s]
 14%|#3        | CSD epoch blocks : 10/72 [00:00<00:05,   12.12it/s]
 15%|#5        | CSD epoch blocks : 11/72 [00:00<00:05,   12.15it/s]
 17%|#6        | CSD epoch blocks : 12/72 [00:00<00:04,   12.23it/s]
 18%|#8        | CSD epoch blocks : 13/72 [00:01<00:04,   12.32it/s]
 19%|#9        | CSD epoch blocks : 14/72 [00:01<00:04,   12.38it/s]
 21%|##        | CSD epoch blocks : 15/72 [00:01<00:04,   12.43it/s]
 22%|##2       | CSD epoch blocks : 16/72 [00:01<00:04,   12.49it/s]
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 25%|##5       | CSD epoch blocks : 18/72 [00:01<00:04,   12.58it/s]
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 31%|###       | CSD epoch blocks : 22/72 [00:01<00:03,   12.78it/s]
 32%|###1      | CSD epoch blocks : 23/72 [00:01<00:03,   12.82it/s]
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 35%|###4      | CSD epoch blocks : 25/72 [00:01<00:03,   12.88it/s]
 36%|###6      | CSD epoch blocks : 26/72 [00:02<00:03,   12.91it/s]
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 50%|#####     | CSD epoch blocks : 36/72 [00:02<00:02,   12.40it/s]
 51%|#####1    | CSD epoch blocks : 37/72 [00:02<00:02,   12.38it/s]
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[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)

Total running time of the script: ( 0 minutes 23.047 seconds)

Estimated memory usage: 258 MB

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