Interpolate bad channels for MEG/EEG channels#

This example shows how to interpolate bad MEG/EEG channels

  • Using spherical splines from [1] for EEG data.

  • Using field interpolation for MEG and EEG data.

In this example, the bad channels will still be marked as bad. Only the data in those channels is replaced.

# Authors: Denis A. Engemann <denis.engemann@gmail.com>
#          Mainak Jas <mainak.jas@telecom-paristech.fr>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import mne
from mne.datasets import sample

print(__doc__)

data_path = sample.data_path()
meg_path = data_path / "MEG" / "sample"
fname = meg_path / "sample_audvis-ave.fif"
evoked = mne.read_evokeds(fname, condition="Left Auditory", baseline=(None, 0))

# plot with bads
evoked.plot(exclude=[], picks=("grad", "eeg"))
EEG (60 channels), Gradiometers (204 channels)
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)

Compute interpolation (also works with Raw and Epochs objects)

evoked_interp = evoked.copy().interpolate_bads(reset_bads=False)
evoked_interp.plot(exclude=[], picks=("grad", "eeg"))
EEG (60 channels), Gradiometers (204 channels)
Setting channel interpolation method to {'eeg': 'spline', 'meg': 'MNE'}.
Interpolating bad channels.
    Automatic origin fit: head of radius 91.0 mm
Computing interpolation matrix from 59 sensor positions
Interpolating 1 sensors
    Computing dot products for 305 MEG channels...
    Computing cross products for 305 → 1 MEG channel...
    Preparing the mapping matrix...
    Truncating at 85/305 components to omit less than 0.0001 (9.4e-05)

You can also use minimum-norm for EEG as well as MEG

evoked_interp_mne = evoked.copy().interpolate_bads(
    reset_bads=False, method=dict(eeg="MNE"), verbose=True
)
evoked_interp_mne.plot(exclude=[], picks=("grad", "eeg"))
EEG (60 channels), Gradiometers (204 channels)
Setting channel interpolation method to {'eeg': 'MNE', 'meg': 'MNE'}.
Interpolating bad channels.
    Automatic origin fit: head of radius 91.0 mm
    Computing dot products for 305 MEG channels...
    Computing cross products for 305 → 1 MEG channel...
    Preparing the mapping matrix...
    Truncating at 85/305 components to omit less than 0.0001 (9.4e-05)
    Computing dot products for 59 EEG channels...
    Computing cross products for 59 → 60 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 (60 channels)

References#

Total running time of the script: (0 minutes 17.304 seconds)

Estimated memory usage: 68 MB

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