Working with ECoG data

MNE supports working with more than just MEG and EEG data. Here we show some of the functions that can be used to facilitate working with electrocorticography (ECoG) data.

This example shows how to use:

• ECoG data (available here) from an epilepsy patient during a seizure

• channel locations in FreeSurfer’s fsaverage MRI space

• projection onto a pial surface

For a complementary example that involves sEEG data, channel locations in MNI space, or projection into a volume, see Working with sEEG data.

# Authors: Eric Larson <larson.eric.d@gmail.com>
#          Chris Holdgraf <choldgraf@gmail.com>
#          Adam Li <adam2392@gmail.com>
#          Alex Rockhill <aprockhill@mailbox.org>
#          Liberty Hamilton <libertyhamilton@gmail.com>
#
# License: BSD-3-Clause

import os.path as op

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
from mne_bids import BIDSPath, read_raw_bids

import mne
from mne.viz import plot_alignment, snapshot_brain_montage

print(__doc__)

# paths to mne datasets - sample ECoG and FreeSurfer subject
bids_root = mne.datasets.epilepsy_ecog.data_path()
sample_path = mne.datasets.sample.data_path()
subjects_dir = op.join(sample_path, 'subjects')

Load in data and perform basic preprocessing

Let’s load some ECoG electrode data with mne-bids.

Note

Downsampling is just to save execution time in this example, you should not need to do this in general!

# first define the bids path
bids_path = BIDSPath(root=bids_root, subject='pt1', session='presurgery',
                     task='ictal', datatype='ieeg', extension='vhdr')

# then we'll use it to load in the sample dataset
# Here we use a format (iEEG) that is only available in MNE-BIDS 0.7+, so it
# will emit a warning on versions <= 0.6
raw = read_raw_bids(bids_path=bids_path, verbose=False)

# Pick only the ECoG channels, removing the EKG channels
raw.pick_types(ecog=True)

# Load the data
raw.load_data()

# Then we remove line frequency interference
raw.notch_filter([60], trans_bandwidth=3)

# drop bad channels
raw.drop_channels(raw.info['bads'])

# the coordinate frame of the montage
montage = raw.get_montage()
print(montage.get_positions()['coord_frame'])

# add fiducials to montage
montage.add_mni_fiducials(subjects_dir)

# now with fiducials assigned, the montage will be properly converted
# to "head" which is what MNE requires internally (this is the coordinate
# system with the origin between LPA and RPA whereas MNI has the origin
# at the posterior commissure)
raw.set_montage(montage)

# Find the annotated events
events, event_id = mne.events_from_annotations(raw)

# Make a 25 second epoch that spans before and after the seizure onset
epoch_length = 25  # seconds
epochs = mne.Epochs(raw, events, event_id=event_id['onset'],
                    tmin=13, tmax=13 + epoch_length, baseline=None)
# Make evoked from the one epoch and resample
evoked = epochs.average().resample(200)
del epochs

Out:

/home/circleci/python_env/lib/python3.8/site-packages/mne_bids/dig.py:539: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation
  raw.set_montage(montage, on_missing='warn')
/home/circleci/python_env/lib/python3.8/site-packages/mne_bids/dig.py:539: RuntimeWarning: DigMontage is only a subset of info. There are 3 channel positions not present in the DigMontage. The required channels are:

['RQ1', 'RQ2', 'N/A'].

Consider using inst.set_channel_types if these are not EEG channels, or use the on_missing parameter if the channel positions are allowed to be unknown in your analyses.
  raw.set_montage(montage, on_missing='warn')
Reading 0 ... 269079  =      0.000 ...   269.079 secs...
Setting up band-stop filter from 58 - 62 Hz

FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal bandstop filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 58.35
- Lower transition bandwidth: 1.50 Hz (-6 dB cutoff frequency: 57.60 Hz)
- Upper passband edge: 61.65 Hz
- Upper transition bandwidth: 1.50 Hz (-6 dB cutoff frequency: 62.40 Hz)
- Filter length: 2201 samples (2.201 sec)

mni_tal
Used Annotations descriptions: ['AD1-4, ATT1,2', 'AST1,3', 'G16', 'PD', 'SLT1-3', 'offset', 'onset']
Not setting metadata
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated

Explore the electrodes on a template brain

Our electrodes are shown after being morphed to fsaverage brain so we’ll use this fsaverage brain to plot the locations of our electrodes. We’ll use snapshot_brain_montage() to save the plot as image data (along with xy positions of each electrode in the image), so that later we can plot frequency band power on top of it.

fig = plot_alignment(raw.info, trans='fsaverage',
                     subject='fsaverage', subjects_dir=subjects_dir,
                     surfaces=['pial'], coord_frame='head')
mne.viz.set_3d_view(fig, azimuth=0, elevation=70)

xy, im = snapshot_brain_montage(fig, raw.info)

Out:

Channel types:: ecog: 84

Compute frequency features of the data

Next, we’ll compute the signal power in the gamma (30-90 Hz) band, downsampling the result to 10 Hz (to save time).

sfreq = 10
gamma_power_t = evoked.copy().filter(30, 90).apply_hilbert(
    envelope=True).resample(sfreq)
gamma_info = gamma_power_t.info

Out:

Setting up band-pass filter from 30 - 90 Hz

FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal bandpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 30.00
- Lower transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 26.25 Hz)
- Upper passband edge: 90.00 Hz
- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 95.00 Hz)
- Filter length: 89 samples (0.445 sec)

Visualize the time-evolution of the gamma power on the brain

Say we want to visualize the evolution of the power in the gamma band, instead of just plotting the average. We can use matplotlib.animation.FuncAnimation to create an animation and apply this to the brain figure.

# convert from a dictionary to array to plot
xy_pts = np.vstack([xy[ch] for ch in raw.info['ch_names']])

# get a colormap to color nearby points similar colors
cmap = get_cmap('viridis')

# create the figure of the brain with the electrode positions
fig, ax = plt.subplots(figsize=(5, 5))
ax.set_title('Gamma power over time', size='large')
ax.imshow(im)
ax.set_axis_off()

# normalize gamma power for plotting
gamma_power = -100 * gamma_power_t.data / gamma_power_t.data.max()
# add the time course overlaid on the positions
x_line = np.linspace(-0.025 * im.shape[0], 0.025 * im.shape[0],
                     gamma_power_t.data.shape[1])
for i, pos in enumerate(xy_pts):
    x, y = pos
    color = cmap(i / xy_pts.shape[0])
    ax.plot(x_line + x, gamma_power[i] + y, linewidth=0.5, color=color)

We can project gamma power from the sensor data to the nearest locations on the pial surface and visualize that:

As shown in the plot, the epileptiform activity starts in the temporal lobe, progressing posteriorly. The seizure becomes generalized eventually, after this example short time section. This dataset is available using mne.datasets.epilepsy_ecog.data_path() for you to examine.

xyz_pts = np.array([dig['r'] for dig in evoked.info['dig']])

src = mne.read_source_spaces(
    op.join(subjects_dir, 'fsaverage', 'bem', 'fsaverage-ico-5-src.fif'))
stc = mne.stc_near_sensors(gamma_power_t, trans='fsaverage',
                           subject='fsaverage', src=src,
                           mode='nearest', subjects_dir=subjects_dir,
                           distance=0.02)
vmin, vmid, vmax = np.percentile(gamma_power_t.data, [10, 25, 90])
clim = dict(kind='value', lims=[vmin, vmid, vmax])
brain = stc.plot(surface='pial', hemi='rh', colormap='inferno', colorbar=False,
                 clim=clim, views=['lat', 'med'], subjects_dir=subjects_dir,
                 size=(250, 250), smoothing_steps='nearest',
                 time_viewer=False)
brain.add_sensors(raw.info, trans='fsaverage')

# You can save a movie like the one on our documentation website with:
# brain.save_movie(time_dilation=1, interpolation='linear', framerate=3,
#                  time_viewer=True)

Once Loop Reflect

Out:

    Reading a source space...
    [done]
    Reading a source space...
    [done]
    2 source spaces read
Projecting data from 84 sensors onto 20484 surface vertices: nearest mode
    Projecting electrodes onto surface
    Minimum projected intra-sensor distance: 1.1 mm
    5323 / 20484 non-zero vertices
Channel types:: ecog: 84
Channel types:: ecog: 84

Estimated memory usage: 649 MB

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Working with sEEG data

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Sleep stage classification from polysomnography (PSG) data