[Eeglablist] automatized ICA data correction and channel interpolation
Luca Pion-Tonachini
lpionton at ucsd.edu
Mon Jul 3 10:31:04 PDT 2017
Hi Makoto,
Just to clarify, while the process of developing my method does use
inputs from the ICLabel website, the final product that people will use
will */only/* look at the EEG measures and nothing from the website.
Just like the other methods, it will not require people to actively
contribute labels to work once released. The label collection is only
for the development of the classier.
Luca
On 06/30/2017 05:52 PM, Makoto Miyakoshi wrote:
> Dear Marius,
>
> Thank you for your opinion.
>
> My colleague Luca has been working on automatic IC labeling using
> machine learning algorithm: but it still uses /user input /as the data
> to be tranied. So it is NOT a solution to develop a better or perfect
> algorithm that judges what is what based on EEG measures (he tested
> all the kinds of algorithms available at the timepoint of last year,
> and found nothing was perfect.) See this page for his data collection
> scheme (which is also a educational tool)
>
> http://reaching.ucsd.edu:8000/auth/login
>
> I have been collaborating with radiologists and neurologists. They
> diagnose patients, particularly neurologists even determines which
> brain tissue to remove. What algorithm do they use? They use eyeballs.
> After all, humans are still the best learning machine today (though it
> is very tempting to make my own criteria for IC labeling for
> non-aggressive data cleaning)
>
> Makoto
>
> On Fri, Jun 30, 2017 at 12:54 AM, Marius Klug <marius.s.klug at gmail.com
> <mailto:marius.s.klug at gmail.com>> wrote:
>
> 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! ;-)
>
> Best,
> Marius
>
>
> 2017-06-30 4:02 GMT+02:00 Makoto Miyakoshi <mmiyakoshi at ucsd.edu
> <mailto: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_rawdataplug-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_interpwhich makes use of Legendre polynomials up
> to degree 7 to calculate unbiased expected channel values (see
> Mullen et al., 2015).
>
> Makoto
>
>
>
> On Mon, Mar 27, 2017 at 7:58 AM, Gian Marco Duma
> <gmduma90 at gmail.com <mailto:gmduma90 at gmail.com>> wrote:
>
> 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
>
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>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
>
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