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%matplotlib inline

Transform EEG data using current source density (CSD)

This script shows an example of how to use CSD [1] [2] [3]_. 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.



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# Authors: Alex Rockhill <aprockhill206@gmail.com>
#
# 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



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raw = mne.io.read_raw_fif(data_path + '/MEG/sample/sample_audvis_raw.fif',
                          preload=True)
events = mne.find_events(raw)
raw = raw.pick_types(meg=False, eeg=True, eog=True, ecg=True, stim=False,
                     exclude=raw.info['bads'])
raw.set_eeg_reference(projection=True).apply_proj()

Plot the raw data and CSD-transformed raw data:



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

Also look at the power spectral densities:



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raw.plot_psd()
raw_csd.plot_psd()

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



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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=.5,
                    preload=True)
evoked = epochs['auditory'].average()

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



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times = np.array([-0.1, 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:



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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], outlines='skirt', contours=4, time_unit='s',
            colorbar=False, show=False)
        ax[i, j].set_title('stiffness=%i\nλ²=%s' % (m, lambda2))

References

.. [1] Perrin F, Bertrand O, Pernier J. "Scalp current density mapping: Value and estimation from potential data." IEEE Trans Biomed Eng. 1987;34(4):283–288. .. [2] Perrin F, Pernier J, Bertrand O, Echallier JF. "Spherical splines for scalp potential and current density mapping." [Corrigenda EEG 02274, EEG Clin. Neurophysiol., 1990, 76, 565] Electroenceph Clin Neurophysiol. 1989;72(2):184–187. .. [3] Kayser J, Tenke CE. "On the benefits of using surface Laplacian (Current Source Density) methodology in electrophysiology. Int J Psychophysiol. 2015 Sep; 97(3): 171–173.