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

[Scott Makeig]

Also, if you use ICA to transform your data into source space, then it is
best to remove channels with (non-physiological) extreme values beforehand
- but then you need not interpolate, since the IC source waveforms are then
the phenomena of interest.

Scott

On Sat, Aug 27, 2016 at 2:18 AM, PERNET Cyril <cyril.pernet at ed.ac.uk> wrote:

> Hi Jenny,
>
>
> Just a quick word to say that once you removed bad channel - you don't
> have to interpolate if you use the LIMO EEG plug-in. Group level statistics
> accounts for missing channels across subjects when computing the null
> distribution for cluster stats. That means that if you have many subjects
> (say > 20) then you can be a little more stringent if you wish too.
>
>
> Cyril
>
>
>
> --
> Dr Cyril Pernet,
> Senior Academic Fellow, Neuroimaging Sciences
> Centre for Clinical Brain Sciences (CCBS)
>
> The University of Edinburgh
> Chancellor's Building, Room GU426D
> 49 Little France Crescent
> Edinburgh EH16 4SB
> cyril.pernet at ed.ac.uk
> <http://www.sbirc.ed.ac.uk/cyril>http://www.sbirc.ed.ac.uk/cyril
> http://www.ed.ac.uk/edinburgh-imaging
>
>
>
> ------------------------------
> *From:* Tarik S Bel-Bahar <tarikbelbahar at gmail.com>
> *Sent:* 24 August 2016 22:16
> *To:* Jenny Bress
> *Cc:* eeglablist
> *Subject:* Re: [Eeglablist] Removing bad channels before average
> referencing - how stringent should I be?
>
> 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
>
>
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-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation, University of
California San Diego, La Jolla CA 92093-0961, http://sccn.ucsd.edu/~scott
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