Interpolate MEG or EEG data to any montage

This example demonstrates both EEG montage interpolation and MEG system transformation.

For EEG, this can be useful for standardizing EEG channel layouts across different datasets (see [1]).

• Using the field interpolation for EEG data.

• Using the target montage “biosemi16”.

• Using the MNE interpolation for MEG data to transform from Neuromag (planar gradiometers and magnetometers) to CTF (axial gradiometers).

In the first example, the data from the original EEG channels will be interpolated onto the positions defined by the “biosemi16” montage.

In the second example, we will interpolate MEG data from a 306-sensor Neuromag to 275-sensor CTF system.

# Authors: Antoine Collas <contact@antoinecollas.fr>
#          Konstantinos Tsilimparis <konstantinos.tsilimparis@outlook.com>
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import matplotlib.pyplot as plt

import mne
from mne.channels import make_standard_montage
from mne.datasets import sample

print(__doc__)
ylim = (-10, 10)

Part 1: EEG System Transformation

# Load EEG data
data_path = sample.data_path()
eeg_file_path = data_path / "MEG" / "sample" / "sample_audvis-ave.fif"
evoked = mne.read_evokeds(eeg_file_path, condition="Left Auditory", baseline=(None, 0))

# Select only EEG channels
evoked.pick("eeg")

# Plot the original EEG layout
evoked.plot(exclude=[], picks="eeg", ylim=dict(eeg=ylim))

Define the target montage

standard_montage = make_standard_montage("biosemi16")

Use interpolate_to to project EEG data to the standard montage

evoked_interpolated_spline = evoked.copy().interpolate_to(
    standard_montage, method="spline"
)

# Plot the interpolated EEG layout
evoked_interpolated_spline.plot(exclude=[], picks="eeg", ylim=dict(eeg=ylim))

Use interpolate_to to project EEG data to the standard montage

evoked_interpolated_mne = evoked.copy().interpolate_to(standard_montage, method="MNE")

# Plot the interpolated EEG layout
evoked_interpolated_mne.plot(exclude=[], picks="eeg", ylim=dict(eeg=ylim))

Comparing before and after interpolation

fig, axs = plt.subplots(3, 1, figsize=(8, 6), constrained_layout=True)
evoked.plot(exclude=[], picks="eeg", axes=axs[0], show=False, ylim=dict(eeg=ylim))
axs[0].set_title("Original EEG Layout")
evoked_interpolated_spline.plot(
    exclude=[], picks="eeg", axes=axs[1], show=False, ylim=dict(eeg=ylim)
)
axs[1].set_title("Interpolated to Standard 1020 Montage using spline interpolation")
evoked_interpolated_mne.plot(
    exclude=[], picks="eeg", axes=axs[2], show=False, ylim=dict(eeg=ylim)
)
axs[2].set_title("Interpolated to Standard 1020 Montage using MNE interpolation")

Part 2: MEG System Transformation

We demonstrate transforming MEG data from Neuromag (planar gradiometers and magnetometers) to CTF (axial gradiometers) sensor configuration.

# Load the full evoked data with MEG channels
evoked_meg = mne.read_evokeds(
    eeg_file_path, condition="Left Auditory", baseline=(None, 0)
)
evoked_meg.pick("meg")

print("Original Neuromag system:")
print(f"  Number of magnetometers: {len(mne.pick_types(evoked_meg.info, meg='mag'))}")
print(f"  Number of gradiometers: {len(mne.pick_types(evoked_meg.info, meg='grad'))}")

Transform to CTF sensor configuration

# Interpolate Neuromag to CTF
evoked_ctf = evoked_meg.copy().interpolate_to("ctf275", mode="accurate")

print("\nTransformed to CTF system:")
print(f"  Number of MEG channels: {len(mne.pick_types(evoked_ctf.info, meg=True))}")
print(f"  Bad channels in original: {evoked_meg.info['bads']}")

Compare evoked responses: Original Neuromag vs Transformed CTF The data should be similar but projected onto different sensor arrays

# Set consistent y-limits for comparison
ylim_meg = dict(grad=[-300, 300], mag=[-600, 600], meg=[-300, 300])

fig, axes = plt.subplots(3, 1, figsize=(10, 8), layout="constrained")

# Plot original Neuromag gradiometers
evoked_meg.copy().pick("grad").plot(
    axes=axes[0], show=False, spatial_colors=True, ylim=ylim_meg, time_unit="s"
)
axes[0].set_title("Original Neuromag Planar Gradiometers", fontsize=14)


# Plot original Neuromag magnetometers
evoked_meg.copy().pick("mag").plot(
    axes=axes[1], show=False, spatial_colors=True, ylim=ylim_meg, time_unit="s"
)
axes[1].set_title("Original Neuromag Magnetometers", fontsize=14)

# Plot transformed CTF gradiometers
evoked_ctf.plot(
    axes=axes[2], show=False, spatial_colors=True, ylim=ylim_meg, time_unit="s"
)
axes[2].set_title("Transformed to CTF275 Axial Gradiometers", fontsize=14)

References

[1]

Apolline Mellot, Antoine Collas, Sylvain Chevallier, Denis Engemann, and Alexandre Gramfort. Physics-informed and unsupervised riemannian domain adaptation for machine learning on heterogeneous eeg datasets. In Proceedings of the 32nd European Signal Processing Conference (EUSIPCO). Lyon, France, 2024.

Estimated memory usage: 0 MB