Interpolate EEG data to any montage

This example demonstrates how to interpolate EEG channels to match a given montage. 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”.

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

# Authors: Antoine Collas <contact@antoinecollas.fr>
# 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)

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))

Reading /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-ave.fif ...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left Auditory)
        0 CTF compensation matrices available
        nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)

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))

Automatic origin fit: head of radius 91.0 mm

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))

Creating RawArray with float64 data, n_channels=16, n_times=1
    Range : 0 ... 0 =      0.000 ...     0.000 secs
Ready.
    Automatic origin fit: head of radius 91.0 mm
    Computing dot products for 59 EEG channels...
    Computing cross products for 59 → 16 EEG channels...
    Preparing the mapping matrix...
    Truncating at 58/59 components and regularizing with α=1.0e-01
    The map has an average electrode reference (16 channels)

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")

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.