1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
|
# -*- coding: utf-8 -*-
"""
.. _ex-interpolate-bad-channels:
=============================================
Interpolate bad channels for MEG/EEG channels
=============================================
This example shows how to interpolate bad MEG/EEG channels
- Using spherical splines from :footcite:`PerrinEtAl1989` 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
# %%
# sphinx_gallery_thumbnail_number = 2
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
# ----------
# .. footbibliography::
|
