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
# Copyright the MNE-Python contributors.

import matplotlib.pyplot as plt
import numpy as np

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(picks=["eeg", "eog", "ecg", "stim"], exclude="bads").load_data()
events = mne.find_events(raw)
raw.set_eeg_reference(projection=True).apply_proj()

Plot the raw data and CSD-transformed raw data:

raw_csd = mne.preprocessing.compute_current_source_density(raw)
raw.plot()
raw_csd.plot()

Also look at the power spectral densities:

raw.compute_psd().plot(picks="data", exclude="bads", amplitude=False)
raw_csd.compute_psd().plot(picks="data", exclude="bads", amplitude=False)

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()

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")

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, layout="constrained")
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(f"stiffness={m}\nλ²={lambda2}")

References

[1]

F. Perrin, O. Bertrand, and J. Pernier. Scalp Current Density Mapping: Value and Estimation from Potential Data. IEEE Transactions on Biomedical Engineering, BME-34(4):283–288, 1987. doi:10.1109/TBME.1987.326089.

[2]

François M. Perrin, Jacques Pernier, Olivier M. Bertrand, and Jean Franćois Echallier. Spherical splines for scalp potential and current density mapping. Electroencephalography and Clinical Neurophysiology, 72(2):184–187, 1989. doi:10.1016/0013-4694(89)90180-6.

[3]

Mike X. Cohen. Analyzing Neural Time Series Data: Theory and Practice. MIT Press, 2014.

[4]

Jürgen Kayser and Craig E. Tenke. On the benefits of using surface Laplacian (Current Source Density) methodology in electrophysiology. International journal of psychophysiology : official journal of the International Organization of Psychophysiology, 97(3):171–173, 2015. doi:10.1016/j.ijpsycho.2015.06.001.

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