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

[Gilbert, David G]

A related question I have concerns the fact that ICA uses percent probability of being EEG, heart, EMG, other, etc. Can you refer me to how these percentages were calculated. I believe that there were a number of experts, but were they looking at epoch-by-epoch portions of the data just what..
Thanks in advance,
David Gilbert
________________________________
From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> on behalf of Scott Makeig <smakeig at gmail.com>
Sent: Thursday, February 23, 2023 2:01 PM
To: Michael Wenger <Michael.J.Wenger at ou.edu>
Cc: eeglablist at sccn.ucsd.edu <eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] simple question: logic of using ICLABEL to reject components

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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, https://urldefense.com/v3/__https://nam11.safelinks.protection.outlook.com/?url=http*3A*2F*2Fsccn.ucsd.edu*2F*scott&data=05*7C01*7Cdgilbert*40siu.edu*7Cdc7810d99a1449af03ce08db15d8f8de*7Cd57a98e7744d43f9bc9108de1ff3710d*7C0*7C0*7C638127793852617383*7CUnknown*7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0*3D*7C3000*7C*7C*7C&sdata=nqxK94tbS4myAbBMDd0eKnmzf8VV0QZiC2jKatgITZY*3D&reserved=0__;JSUlJX4lJSUlJSUlJSUlJSUlJSUl!!Mih3wA!BSKxktUnaXNe9EblbtuetZQmfnv5Ay4xwPxFX-Dnkzb7JKdBFYBx9PBDge-dawcCS-TeErtVZsEAEXBr2vbj3lQ$ 
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