[Eeglablist] automatized ICA data correction and channel interpolation

[Makoto Miyakoshi]

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).

Makoto



On Mon, Mar 27, 2017 at 7:58 AM, Gian Marco Duma <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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