[Eeglablist] Removing bad channels before average referencing - how stringent should I be?

[Tarik S Bel-Bahar]

Hello Jenny, some tools, notes, and articles below, Best wishes.

****************************************************
Try/review/check   plugins/documentation/settings/articles on the
following tools.
Each is googlable and has online or within-function documentation.

*Within eeglab:
clean_rawdata including it's bad channel detection function,
trimOutlier
PREP plugin that uses ASR andCleanline (see article on this),
Cleanline and ASR plugins  can also be used independently.
Also see FASTER plugin and SASICA plugins/articles

*outside eeglab: for example,
See TAPEEG, standalone and matlab
See SCADS method from Jung et al., implemented in Cartool
See ERPLAB too which should have some form of principled bad channel detection




*****************************extra notes for J
Those are reasonable questions in your case, for which there are no
established and robust standards across the field. Dense EEG has been
around for nearly 20 years, so the existing base of published methods
and "usual EEG laboratory methods" should be your first resource. Of
course, researchers usually use their own preferred methods, the
settings for which they only sometimes publish. Further, default
plugins/settings don't always work for finding the bad channels.
You're probably best off using some combination of visual detection by
expert, relying on thresholds/algorithms usually used for bad-channel
detection for your eeg system, reviewing published methods in recent
high-quality articles using an eeg protocol similar to yours, and
extra checking and reiterations on your end. The automated software
for artifact-detection has been getting better, so it will also be
helpful to examine the output of some automatic methods in eeglab and
other tools (see tools above and some of the articles below). Last,
ICA should be very useful for your purposes, as it can pick up
spatially localized artifacts quite well.


A good caveat is to not remove many contiguous channels, so you don't
have a "large patch" with no channels.  Another simple enough method
is to use a human to visually detect the worst 5 to 10% of the
channels. Checking the published methods/thresholds of some groups is
useful, though stringency depends on the lab and researchers and can
be variable. How many channels you can safely remove also depends on
if you have 64, 128, or 256 channels.

ICA is pretty good at picking up stereotyped artifacts in dense eeg,
and often picks up ICs that reflect singel noisy channels or periods
where single channels go bad briefly. Note that before ICA, it's
better not to interpolate the bad channels.Also, ICA-cleaning often
helps a lot with cleaning up channels that have continuous muscle or
electrical noise. ICA-classification plugins, such as SASICA, can help
you detect ICs that capture the activity of one bad channel that
occurs during only some trials.


****************************some example articles for J, each can be
found through google scholar

A practical guide to the selection of independent components of the
electroencephalogram for artifact correctionJournal of neuroscience
methods, 2015 - Elsevier
Automatic artifacts and arousals detection in whole-night sleep EEG
recordings Journal of neuroscience …, 2016 - Elsevier
The PREP pipeline: standardized preprocessing for large-scale
EEGanalysis Frontiers in …, 2015 - ncbi.nlm.nih.gov
Reliability of fully automated versus visually controlled pre-and
post-processing of resting-state EEGClinical …, 2015 - Elsevier
Comparing the Performance of Popular MEG/EEG Artifact Correction
Methods in an Evoked-Response StudyComputational …, 2016 - dl.acm.org
Automated rejection and repair of bad trials in MEG/EEG6th
International …, 2016 - hal.archives-ouvertes.fr
Hybrid wavelet and EMD/ICA approach for artifact suppression in
pervasive EEGJournal of neuroscience methods, 2016 - Elsevier