mne-python/examples/preprocessing/plot_eeg_csd.py at master · cjayb/mne-python · GitHub

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"""
=====================================================
Transform EEG data using current source density (CSD)
=====================================================

This script shows an example of how to use CSD
:footcite`PerrinEtAl1987,PerrinEtAl1989,Cohen2014,KayserTenke2015`.
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)

# sphinx_gallery_thumbnail_number = 6

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
raw = mne.io.read_raw_fif(data_path + '/MEG/sample/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()

###############################################################################
# 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.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.

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:

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:

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
# ----------
# .. footbibliography::