[Eeglablist] simple question: logic of using ICLABEL to reject components

[Makoto Miyakoshi]

Dear Michael,

Lisa pointed me to this post.

See this paper. In Figure 4, it shows Brain 53%, Muscle 12%, Eye 9%,
Channel Noise <1%, Line noise <1%, Heart < 1%, Other 24% for awake state.
During the sleep state, Brain 74%, Other 23%, everything else < 2%. This is
for the case of 19 channels.

Automated preprocessing and phase-amplitude coupling analysis of scalp EEG
discriminates infantile spasms from controls during wakefulness.
Miyakoshi M, Nariai H, Rajaraman RR, Bernardo D, Shrey DW, Lopour BA, Sim
MS, Staba RJ, Hussain SA
Epilepsy Res. 2021 Dec; 178 106809
DOI: 10.1016/j.eplepsyres.2021.106809, PMID: 34823159

See also this Wikipedia article. It shows Brain 52%, Muscle 30%, Eye 6%,
Heart 2%, Other 11%. This is for the case of 38 channels.

https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline#Does_a_broad_dipole_layer_produce_a_depth_bias_when_fitted_with_a_single_dipole.3F_.28For_190.2C000_page_views.2C_02.2F22.2F2022_added.2C_07.2F18.2F2022_updated.29

I have several other unpublished datasets with 64 channels (the LEMON
datasets; Babayan et al., 2019. *Scientific Data*). They also showed Brain
class around 55%. Although this cross-number-of-channel test is not
official, my impression is that the EEG data with standard quality seem to
show 50-55% of Brain class rate regardless of the number of channels.

I also calculate percent variance of the Brain etc. classes, but these are
not published. I will next time.

Makoto

On Fri, Feb 24, 2023 at 1:01 PM Michael Wenger <Michael.J.Wenger at ou.edu>
wrote:

> Scott -- thanks for your note, and this is very helpful. We'll re-examine
> our data and our pre-processing per your suggestion. It is the case that
> the components identified as brain account for much more of the data than
> do the components labeled as other. Thanks again,
>
> -Michael
>
> -----
>
> M. J. Wenger, Ph.D.
> Department of Psychology
> Graduate Program in Cellular and Behavioral Neurobiology
> Stephenson Cancer Center, Cancer Prevention and Control
> The University of Oklahoma
> Norman OK 73019
>
> phone: +1 405 325 0770
> e-mail: m <Michael.J.Wenger at ou.edu>ichael.j.wenger at ou.edu
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>
>
> On Thu, Feb 23, 2023 at 2:02 PM Scott Makeig <smakeig at gmail.com> wrote:
>
> > ICLABEL compares component properties in your data (e.g., component scalp
> > projection maps, power spectra) with datasets in its large training data
> > (mainly datasets from our 20-yr history of applying ICA decomposition to
> > EEG data at SCCN). ICA decomposition can be negatively affected by
> several
> > factors:  too little data, abundant non-brain noise in the data, etc. -
> and
> > by the conditions under which it was recorded (e.g., Were participants
> > moving? Were the electrodes securely placed? etc.).
> >
> > So the first thing I would suggest you look at is whether your data
> > preprocessing and data rejection process was adequate for the data.
> > Next, I would suggest you see how much of the data is accounted for by
> the
> > labeled Brain components plus the non-brain components of known origin
> > (e.g., Eye Movement components).
> > Many times components rated as 'Other' by ICLabel account for quite
> little
> > of the data (e.g., single-channel ICs) - the *de facto* decomposition
> > noise subspace.
> > If you perform PCA decomposition on your dataset and look at the values
> of
> > the resulting eigenvalue spectrum, you will typically find that a large
> > proportion of EEG data 'lives' in relatively few dimensions - ICA
> > decompositions finds a basis for this subspace such that each basis
> element
> > (Independent Component) is as temporally distinct from the others as
> > possible -- and is thereby typically *functionally* distinct from others,
> > And *spatially* distinct from others.
> >
> > Scott
> >
> > On Thu, Feb 23, 2023 at 12:56 PM Michael Wenger <Michael.J.Wenger at ou.edu
> >
> > wrote:
> >
> >> All -- we've recently begun adding the use of ICLABEL to our
> >> pre-processing
> >> pipeline. What we've found is that it labels only a minority of
> components
> >> as brain activity, with the majority being labeled as "other." If we
> >> reject
> >> the components labeled as non-brain activity, we end up rejecting the
> >> majority of the components, and this seems wrong. Can anyone who has
> been
> >> using ICLABEL comment on how to select components for removal? Thanks in
> >> advance,
> >>
> >> -Michael
> >> _______________________________________________
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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 92093-0559,
> http://sccn.ucsd.edu/~scott
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