Transform EEG data using current source density (CSD)#

This script shows an example of how to use CSD [1][2][3][4]. CSD takes the spatial Laplacian of the sensor signal (derivative in both x and y). It does what a planar gradiometer does in MEG. Computing these spatial derivatives reduces point spread. CSD transformed data have a sharper or more distinct topography, reducing the negative impact of volume conduction.

# Authors: Alex Rockhill <aprockhill@mailbox.org>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt

import mne
from mne.datasets import sample

print(__doc__)

data_path = sample.data_path()

Load sample subject data

meg_path = data_path / "MEG" / "sample"
raw = mne.io.read_raw_fif(meg_path / "sample_audvis_raw.fif")
raw = raw.pick_types(
    meg=False, eeg=True, eog=True, ecg=True, stim=True, exclude=raw.info["bads"]
).load_data()
events = mne.find_events(raw)
raw.set_eeg_reference(projection=True).apply_proj()
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.
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
Removing projector <Projection | PCA-v1, active : False, n_channels : 102>
Removing projector <Projection | PCA-v2, active : False, n_channels : 102>
Removing projector <Projection | PCA-v3, active : False, n_channels : 102>
Reading 0 ... 166799  =      0.000 ...   277.714 secs...
320 events found
Event IDs: [ 1  2  3  4  5 32]
EEG channel type selected for re-referencing
Adding average EEG reference projection.
1 projection items deactivated
Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it.
Created an SSP operator (subspace dimension = 1)
1 projection items activated
SSP projectors applied...
Measurement date December 03, 2002 19:01:10 GMT
Experimenter MEG
Participant Unknown
Digitized points 146 points
Good channels 9 Stimulus, 59 EEG, 1 EOG
Bad channels None
EOG channels EOG 061
ECG channels Not available
Sampling frequency 600.61 Hz
Highpass 0.10 Hz
Lowpass 172.18 Hz
Projections Average EEG reference : on
Filenames sample_audvis_raw.fif
Duration 00:04:38 (HH:MM:SS)


Plot the raw data and CSD-transformed raw data:

  • Raw plots
  • Raw plots
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm

Also look at the power spectral densities:

raw.compute_psd().plot(picks="data", exclude="bads")
raw_csd.compute_psd().plot(picks="data", exclude="bads")
  • EEG
  • Current source density
Effective window size : 3.410 (s)
Effective window size : 3.410 (s)

CSD can also be computed on Evoked (averaged) data. Here we epoch and average the data so we can demonstrate that.

event_id = {
    "auditory/left": 1,
    "auditory/right": 2,
    "visual/left": 3,
    "visual/right": 4,
    "smiley": 5,
    "button": 32,
}
epochs = mne.Epochs(raw, events, event_id=event_id, tmin=-0.2, tmax=0.5, preload=True)
evoked = epochs["auditory"].average()
Not setting metadata
320 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] s
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 1)
1 projection items activated
Using data from preloaded Raw for 320 events and 421 original time points ...
0 bad epochs dropped
NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).

First let’s look at how CSD affects scalp topography:

times = np.array([-0.1, 0.0, 0.05, 0.1, 0.15])
evoked_csd = mne.preprocessing.compute_current_source_density(evoked)
evoked.plot_joint(title="Average Reference", show=False)
evoked_csd.plot_joint(title="Current Source Density")
  • 0.093 s, 0.205 s, 0.320 s
  • 0.095 s, 0.208 s, 0.310 s
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Projections have already been applied. Setting proj attribute to True.
NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).
Projections have already been applied. Setting proj attribute to True.
NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).

CSD has parameters stiffness and lambda2 affecting smoothing and spline flexibility, respectively. Let’s see how they affect the solution:

fig, ax = plt.subplots(4, 4)
fig.subplots_adjust(hspace=0.5)
fig.set_size_inches(10, 10)
for i, lambda2 in enumerate([0, 1e-7, 1e-5, 1e-3]):
    for j, m in enumerate([5, 4, 3, 2]):
        this_evoked_csd = mne.preprocessing.compute_current_source_density(
            evoked, stiffness=m, lambda2=lambda2
        )
        this_evoked_csd.plot_topomap(
            0.1, axes=ax[i, j], contours=4, time_unit="s", colorbar=False, show=False
        )
        ax[i, j].set_title("stiffness=%i\nλ²=%s" % (m, lambda2))
stiffness=5 λ²=0, stiffness=4 λ²=0, stiffness=3 λ²=0, stiffness=2 λ²=0, stiffness=5 λ²=1e-07, stiffness=4 λ²=1e-07, stiffness=3 λ²=1e-07, stiffness=2 λ²=1e-07, stiffness=5 λ²=1e-05, stiffness=4 λ²=1e-05, stiffness=3 λ²=1e-05, stiffness=2 λ²=1e-05, stiffness=5 λ²=0.001, stiffness=4 λ²=0.001, stiffness=3 λ²=0.001, stiffness=2 λ²=0.001
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm
Fitted sphere radius:         91.0 mm
Origin head coordinates:      -4.1 16.0 51.7 mm
Origin device coordinates:    1.4 17.8 -10.3 mm

References#

Total running time of the script: ( 0 minutes 18.250 seconds)

Estimated memory usage: 218 MB

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