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"))

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"))

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"))

References

[1]

François M. Perrin, Jacques Pernier, Olivier M. Bertrand, and Jean Franćois Echallier. Spherical splines for scalp potential and current density mapping. Electroencephalography and Clinical Neurophysiology, 72(2):184–187, 1989. doi:10.1016/0013-4694(89)90180-6.

Estimated memory usage: 0 MB