This tutorial covers manual marking of bad channels and reconstructing bad channels based on good signals at other sensors.

As usual we’ll start by importing the modules we need, and loading some example data:

import os
from copy import deepcopy
import numpy as np
import mne

sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')


Sometimes individual channels malfunction and provide data that is too noisy to be usable. MNE-Python makes it easy to ignore those channels in the analysis stream without actually deleting the data in those channels. It does this by keeping track of the bad channel indices in a list and looking at that list when doing analysis or plotting tasks. The list of bad channels is stored in the ‘bads’ field of the Info object that is attached to Raw, Epochs, and Evoked objects.

print(raw.info['bads'])


Out:

['MEG 2443', 'EEG 053']


Here you can see that the .fif file we loaded from disk must have been keeping track of channels marked as “bad” — which is good news, because it means any changes we make to the list of bad channels will be preserved if we save our data at intermediate stages and re-load it later. Since we saw above that EEG 053 is one of the bad channels, let’s look at it alongside some other EEG channels to see what’s bad about it. We can do this using the standard plot() method, and instead of listing the channel names one by one (['EEG 050', 'EEG 051', ...]) we’ll use a regular expression to pick all the EEG channels between 050 and 059 with the pick_channels_regexp() function (the . is a wildcard character):

picks = mne.pick_channels_regexp(raw.ch_names, regexp='EEG 05.')
raw.plot(order=picks, n_channels=len(picks))


We can do the same thing for the bad MEG channel (MEG 2443). Since we know that Neuromag systems (like the one used to record the example data) use the last digit of the MEG channel number to indicate sensor type, here our regular expression will pick all the channels that start with 2 and end with 3:

picks = mne.pick_channels_regexp(raw.ch_names, regexp='MEG 2..3')
raw.plot(order=picks, n_channels=len(picks))


Notice first of all that the channels marked as “bad” are plotted in a light gray color in a layer behind the other channels, to make it easy to distinguish them from “good” channels. The plots make it clear that EEG 053 is not picking up scalp potentials at all, and MEG 2443 looks like it’s got a lot more internal noise than its neighbors — its signal is a few orders of magnitude greater than the other MEG channels, making it a clear candidate for exclusion.

If you want to change which channels are marked as bad, you can edit raw.info['bads'] directly; it’s an ordinary Python list so the usual list methods will work:

original_bads = deepcopy(raw.info['bads'])


You can also interactively toggle whether a channel is marked “bad” in the plot windows of raw.plot() or epochs.plot() by clicking on the channel name along the vertical axis (in raw.plot() windows you can also do this by clicking the channel’s trace in the plot area). The bads field gets updated immediately each time you toggle a channel, and will retain its modified state after the plot window is closed.

The list of bad channels in the mne.Info object’s bads field is automatically taken into account in dozens of functions and methods across the MNE-Python codebase. This is done consistently with a parameter exclude='bads' in the function or method signature. Typically this exclude parameter also accepts a list of channel names or indices, so if you want to include the bad channels you can do so by passing exclude=[] (or some other list of channels to exclude). For example:

# default is exclude='bads':
good_eeg = mne.pick_types(raw.info, meg=False, eeg=True)
all_eeg = mne.pick_types(raw.info, meg=False, eeg=True, exclude=[])
print(np.setdiff1d(all_eeg, good_eeg))
print(np.array(raw.ch_names)[np.setdiff1d(all_eeg, good_eeg)])


Out:

[367]
['EEG 053']


### When to look for bad channels¶

You can start looking for bad channels during the experiment session when the data is being acquired. If you notice any flat or excessively noisy channels, you can note them in your experiment log or protocol sheet. If your system computes online averages, these can be a good way to spot bad channels as well. After the data has been collected, you can do a more thorough check for bad channels by browsing the raw data using mne.io.Raw.plot(), with any projectors or ICA applied. Finally, you can compute offline averages (again with projectors, ICA, and EEG referencing disabled) to look for channels with unusual properties. Here’s an example of ERP/F plots where the bad channels were not properly marked:

raw2 = raw.copy()
events = mne.find_events(raw2, stim_channel='STI 014')
epochs = mne.Epochs(raw2, events=events)['2'].average().plot()


Out:

320 events found
Event IDs: [ 1  2  3  4  5 32]
320 matching events found
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 3)
3 projection items activated


Remember, marking bad channels should be done as early as possible in the analysis pipeline. When bad channels are marked in a Raw object, the markings will be automatically transferred through the chain of derived object types: including Epochs and Evoked objects, but also noise covariance objects, forward solution computations, inverse operators, etc. If you don’t notice the badness until later stages of your analysis pipeline, you’ll probably need to go back and re-run the pipeline, so it’s a good investment of time to carefully explore the data for bad channels early on.

### Why mark bad channels at all?¶

Many analysis computations can be strongly affected by the presence of bad channels. For example, a malfunctioning channel with completely flat signal will have zero channel variance, which will cause noise estimates to be unrealistically low. This low noise estimate will lead to a strong channel weight in the estimate of cortical current, and because the channel is flat, the magnitude of cortical current estimates will shrink dramatically.

Conversely, very noisy channels can also cause problems. For example, they can lead to too many epochs being discarded based on signal amplitude rejection thresholds, which in turn can lead to less robust estimation of the noise covariance across sensors. Noisy channels can also interfere with SSP computations, because the projectors will be spatially biased in the direction of the noisy channel, which can cause adjacent good channels to be suppressed. ICA is corrupted by noisy channels for similar reasons. On the other hand, when performing machine learning analyses, bad channels may have limited, if any impact (i.e., bad channels will be uninformative and therefore ignored / deweighted by the algorithm).

In some cases simply excluding bad channels is sufficient (for example, if you plan only to analyze a specific sensor ROI, and the bad channel is outside that ROI). However, in cross-subject analyses it is often helpful to maintain the same data dimensionality for all subjects, and there is no guarantee that the same channels will be bad for all subjects. It is possible in such cases to remove each channel that is bad for even a single subject, but that can lead to a dramatic drop in data rank (and ends up discarding a fair amount of clean data in the process). In such cases it is desirable to reconstruct bad channels by interpolating its signal based on the signals of the good sensors around them.

### How interpolation works¶

Interpolation of EEG channels in MNE-Python is done using the spherical spline method 1, which projects the sensor locations onto a unit sphere and interpolates the signal at the bad sensor locations based on the signals at the good locations. Mathematical details are presented in Bad channel repair via interpolation. Interpolation of MEG channels uses the field mapping algorithms used in computing the forward solution.

### Interpolation in MNE-Python¶

Interpolating bad channels in Raw objects is done with the interpolate_bads() method, which automatically applies the correct method (spherical splines or field interpolation) to EEG and MEG channels, respectively (there is a corresponding method mne.Epochs.interpolate_bads() that works for Epochs objects). To illustrate how it works, we’ll start by cropping the raw object to just three seconds for easier plotting:

raw.crop(tmin=0, tmax=3).load_data()


By default, interpolate_bads() will clear out raw.info['bads'] after interpolation, so that the interpolated channels are no longer excluded from subsequent computations. Here, for illustration purposes, we’ll prevent that by specifying reset_bads=False so that when we plot the data before and after interpolation, the affected channels will still plot in red:

eeg_data = raw.copy().pick_types(meg=False, eeg=True, exclude=[])

for title, data in zip(['orig.', 'interp.'], [eeg_data, eeg_data_interp]):
fig.suptitle(title, size='xx-large', weight='bold')


Note that we used the exclude=[] trick in the call to pick_types() to make sure the bad channels were not automatically dropped from the selection. Here is the corresponding example with the interpolated gradiometer channel; since there are more channels we’ll use a more transparent gray color this time:

grad_data = raw.copy().pick_types(meg='grad', exclude=[])



## Summary¶

Bad channel exclusion or interpolation is an important step in EEG/MEG preprocessing. MNE-Python provides tools for marking and interpolating bad channels; the list of which channels are marked as “bad” is propagated automatically through later stages of processing. For an even more automated approach to bad channel detection and interpolation, consider using the autoreject package, which interfaces well with MNE-Python-based pipelines.

## References¶

1

Perrin, F., Pernier, J., Bertrand, O. and Echallier, JF. (1989). Spherical splines for scalp potential and current density mapping. Electroencephalography Clinical Neurophysiology 72(2):184-187.

Total running time of the script: ( 0 minutes 13.947 seconds)

Estimated memory usage: 67 MB

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