[Eeglablist] automatized ICA data correction and channel interpolation
marius.s.klug at gmail.com
Fri Jun 30 00:54:13 PDT 2017
Hi Gian, Makoto, list
the quest for more reproducibility in EEG is one of my personal topics of
interest, so here are a few comments from my point of view: I too would
consider an automated method for IC classification a valuable tool, and
dismissing it is not as easy as Makoto suggests if you ask me. But I agree
with Makoto: there are no perfect solutions and manual inspection to date
is hard to get around since experienced inspectors are just better than the
algorithms. However the reasoning that it's not reproducible is valid, and
also maybe a trained eye is better than automated methods, but a novice
might not exactly know where to look at and the quality may suffer...
You didn't specify if you just want to reject eye-movement and other
artifacts or want to go the other way round and just keep ICs that are
specifically generated by the brain. In the latter case you might want to
try out a few things, because there are some that work okay-ish:
First, you can use dipole fitting and check the residual variance for each
IC, and have a threshold of <0.15 (typically) for brain ICs. This is not a
fail-safe method, however, since eye-ICs and sometimes also very narrow
(not spread around the whole head) muscle or other artifactual ICs can have
a small residual variance. You can also check if the located dipoles lie
inside the brain of the dipfit model.
Then there's SASICA, an EEGLAB plug-in also published (Chaumon, Bishop &
Busch, 2015). I highly recommend the paper also for a deeper understanding
of the IC classification. Playing around with SASICA I found it to be okay
- not perfect - for an automated method. You can play around with the
different classifiers (it uses several methods in combination - dipfit
being one of them, but not mandatory) and their respective thresholds and
check if it suits you.
Unfortunately, EEG methods do have a high degree of subjectivity in several
steps, and the automated methods are usually not as good as manual
inspection yet (bad channel detection and time-domain artifacts as well),
but it's a trade-off between clear reproducibility and best quality, the
topic as a whole needs to be treated with care. I hope you find some
valuable information in my suggestions and wish you a successful study and
smooth data analysis process! ;-)
2017-06-30 4:02 GMT+02:00 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>:
> Dear Gian Marco,
> Ah I missed your important email! Sorry for being so late.
> Automated ICA rejection is not more reliable than manual inspection,
> because the programmer implemented his or her own criteria (or his or her
> research results on criteria) to the application. Therefore, for rebuttal
> you can say 'Then why do neurologists still use their eyeballs to identify
> epileptic spikes, given the great advance of machine learning technology
> today' etc... Trained eyes are still one of the best solutions.
> > Another question is about bad channel interpolation. In EEGlab there is
> the kurtosis method but it does not work so good. Do you known any other
> automatic method that recognize bad channel?
> I recommend either the one implemented in clean_rawdata() plugin or the
> one in PREP plugin. The former was developed by Christian Kothe and the
> other by Nima Bigdely-Shamlo, both are former SCCN colleagues (now in
> Qusp). Let me share a piece of my recent writing.
> We first performed outlier channel detection, rejection, and interpolation
> using the clean_rawdata plug-in (contributed by Christian Kothe) also
> available through the EEGLAB Extension Manager. This plug-in calculates
> each scalp channel signal’s correlation to its random sample consensus
> (RANSAC) estimate computed from nearby scalp channel signals in successive
> 5-s segments. Channel signals exhibiting low correlation to signals in
> neighboring scalp channels (e.g., here r < 0.8 at more than 40% of the data
> points) were rejected and then replaced with an interpolated channel using
> the spherical option in eeg_interp which makes use of Legendre
> polynomials up to degree 7 to calculate unbiased expected channel values
> (see Mullen et al., 2015).
> On Mon, Mar 27, 2017 at 7:58 AM, Gian Marco Duma <gmduma90 at gmail.com>
>> Dear EEGlab community, I'm writing because I need a suggestion. ICA works
>> very well for eye blink and eye movements correction, and indeed it works
>> very well too for artifacts identification as muscle contraction,
>> electrical noise and so on. Thanks to the experience it becomes possible to
>> recognize the specific components by visual inspection, even if an
>> experimenter must be very careful in components rejection. I submitted a
>> pre-registered reports to Cortex journal, and they asked me for an
>> automatized ICA components rejection method because visual inspection is
>> not considered as a reproducible method. So I'm writing to ask for a
>> suggetion about possible automatized components rejection methods,
>> specially for eye blink and eye movements.
>> Another question is about bad channel interpolation. In EEGlab there is
>> the kurtosis method but it does not work so good. Do you known any other
>> automatic method that recognize bad channel?
>> Thanks for your help
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> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.
> For digest mode, send an email with the subject "set digest mime" to
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