Note
Click here to download the full example code
This example shows how to interpolate bad MEG/EEG channels
- Using spherical splines as described in [1] for EEG data.
- Using field interpolation for MEG data.
The bad channels will still be marked as bad. Only the data in those channels is removed.
| [1] | Perrin, F., Pernier, J., Bertrand, O. and Echallier, JF. (1989) Spherical splines for scalp potential and current density mapping. Electroencephalography and Clinical Neurophysiology, Feb; 72(2):184-7. |
Out:
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)
# Authors: Denis A. Engemann <denis.engemann@gmail.com>
# Mainak Jas <mainak.jas@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
evoked = mne.read_evokeds(fname, condition='Left Auditory',
baseline=(None, 0))
# plot with bads
evoked.plot(exclude=[], time_unit='s')
# compute interpolation (also works with Raw and Epochs objects)
evoked.interpolate_bads(reset_bads=False, verbose=False)
# plot interpolated (previous bads)
evoked.plot(exclude=[], time_unit='s')
Total running time of the script: ( 0 minutes 8.198 seconds)
Estimated memory usage: 10 MB