Display sensitivity maps for EEG and MEG sensors

Sensitivity maps can be produced from forward operators that indicate how well different sensor types will be able to detect neural currents from different regions of the brain.

To get started with forward modeling see Head model and forward computation.

# Author: Eric Larson <larson.eric.d@gmail.com>
#
# 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
from mne.source_estimate import SourceEstimate
from mne.source_space import compute_distance_to_sensors

print(__doc__)

data_path = sample.data_path()
meg_path = data_path / "MEG" / "sample"
fwd_fname = meg_path / "sample_audvis-meg-eeg-oct-6-fwd.fif"
subjects_dir = data_path / "subjects"

# Read the forward solutions with surface orientation
fwd = mne.read_forward_solution(fwd_fname)
mne.convert_forward_solution(fwd, surf_ori=True, copy=False)
leadfield = fwd["sol"]["data"]
print("Leadfield shape : {leadfield.shape}")

Compute sensitivity maps

grad_map = mne.sensitivity_map(fwd, ch_type="grad", mode="fixed")
mag_map = mne.sensitivity_map(fwd, ch_type="mag", mode="fixed")
eeg_map = mne.sensitivity_map(fwd, ch_type="eeg", mode="fixed")

Show gain matrix a.k.a. leadfield matrix with sensitivity map

picks_meg = mne.pick_types(fwd["info"], meg=True, eeg=False)
picks_eeg = mne.pick_types(fwd["info"], meg=False, eeg=True)

fig, axes = plt.subplots(2, 1, figsize=(10, 8), sharex=True)
fig.suptitle("Lead field matrix (500 dipoles only)", fontsize=14)
for ax, picks, ch_type in zip(axes, [picks_meg, picks_eeg], ["meg", "eeg"]):
    im = ax.imshow(leadfield[picks, :500], origin="lower", aspect="auto", cmap="RdBu_r")
    ax.set_title(ch_type.upper())
    ax.set_xlabel("sources")
    ax.set_ylabel("sensors")
    fig.colorbar(im, ax=ax)

fig_2, ax = plt.subplots()
ax.hist(
    [grad_map.data.ravel(), mag_map.data.ravel(), eeg_map.data.ravel()],
    bins=20,
    label=["Gradiometers", "Magnetometers", "EEG"],
    color=["c", "b", "k"],
)
fig_2.legend()
ax.set(title="Normal orientation sensitivity", xlabel="sensitivity", ylabel="count")

brain_sens = grad_map.plot(
    subjects_dir=subjects_dir, clim=dict(lims=[0, 50, 100]), figure=1
)
brain_sens.add_text(0.1, 0.9, "Gradiometer sensitivity", "title", font_size=16)

Compare sensitivity map with distribution of source depths

# source space with vertices
src = fwd["src"]

# Compute minimum Euclidean distances between vertices and MEG sensors
depths = compute_distance_to_sensors(src=src, info=fwd["info"], picks=picks_meg).min(
    axis=1
)
maxdep = depths.max()  # for scaling

vertices = [src[0]["vertno"], src[1]["vertno"]]

depths_map = SourceEstimate(data=depths, vertices=vertices, tmin=0.0, tstep=1.0)

brain_dep = depths_map.plot(
    subject="sample",
    subjects_dir=subjects_dir,
    clim=dict(kind="value", lims=[0, maxdep / 2.0, maxdep]),
    figure=2,
)
brain_dep.add_text(0.1, 0.9, "Source depth (m)", "title", font_size=16)

Sensitivity is likely to co-vary with the distance between sources to sensors. To determine the strength of this relationship, we can compute the correlation between source depth and sensitivity values.

corr = np.corrcoef(depths, grad_map.data[:, 0])[0, 1]
print(f"Correlation between source depth and gradiomter sensitivity values: {corr:f}.")

Gradiometer sensitiviy is highest close to the sensors, and decreases rapidly with inreasing source depth. This is confirmed by the high negative correlation between the two.

Estimated memory usage: 0 MB