[Eeglablist] ICLabel: "source" explanation of "channel noise" independent components in the absence of obviously bad data?

[Scott Burwell]

Thanks, Scott, for your response and happy new year to you too.

For background (to the uninitiated), ICLabel returns a row of probabilities
for each component that its source "class" is brain, muscle, eye, heart,
line noise, channel noise, or other non-brain noise. Upon ICLabel returning
source class probabilities for each component, there is a question as to
how to go about filtering one's data to only "brain" components (e.g., for
downstream scalp or source analyses). I have been testing out different
cutoffs for which to keep only "brain" components, but have found this to
feel a bit arbitrary (e.g., keep components with brain probability > .90? >
.75?). So, the approach I've taken lately is to decide a component's class
for which its probability is the greatest (see below code snippet).

EEG = iclabel(EEG);            %run the classifier
sourcetypes = [1 2 3 4 5 6 7]; %brain, muscle, eye, etc.
[~,idx] =
max(EEG.etc.ic_classification.ICLabel.classifications(:,sourcetypes)');
%assign class based on maximum probability
EEG = pop_subcomp(EEG,find(idx==1),0,1); %filter out all non-brain
components

The frequency of each class type returned by ICLabel is:
Value         Count    Percent  eeg_pvaf (range)
Brain            30     50.85%  -0.56 to  2.10%
Muscle           11     18.64%  -0.06 to 22.21%
Eye               2      3.39%   1.24 to 26.29%
Heart             0      0.00%   -
Line noise        0      0.00%   -
Chan noise        3      5.08%   0.13 to  1.42%
Other non-brain  13     22.03%   -0.22 to 0.67%

In this relatively clean looking resting-state dataset, ~50% of components
are classified as "brain," ~19% as "muscle," and ~3% as "eye," which upon
my visual inspection appear to be accurate. Additionally, a substantial
percentage of components are classified as "channel noise" (~5%) and "other
non-brain" (~22%), which I am finding difficult to explain / justify
exclusion of in a manuscript I am writing, especially when the channel data
appear to be clean. The percent variance accounted for by the "channel
noise" and "other non-brain" components is small (<2%), but does not seem
to be substantially different from the percent variance accounted for by
components classified as "brain." Additionally, the time-series /
activations and frequency spectra for the "channel noise" and "other
non-brain" components do not appear to be terribly noisy or different next
to that of some "brain" components.

I have thought about the possibility of only considering a *subset* of
ICLabel columns in deciding their class. E.g., in the above code,
specifying sourcetypes = [1 2 3 4 5], effectively forcing "channel noise"
and "other non-brain" components to be assigned to one of the easier to
interpret classes (i.e., brain, muscle, eye, heart, line noise), but not
sure how this would be received by others.

Your thoughts would be appreciated.

Warmest regards and a happy new year!
Scott



On Tue, Dec 31, 2019 at 10:40 PM Scott Makeig <smakeig at ucsd.edu> wrote:

> Scott -  I'd need to know how much of the seeming-good channel data are
> accounted for by the suggested 'channel-noise' IC?  Only a small % ... ?
>
> Happy New Year
>
> Scott
>
> On Mon, Dec 30, 2019 at 10:14 PM Scott Burwell <burwell at umn.edu> wrote:
>
>> Hello,
>>
>> I have been testing the ICLabel plug-in on some data (very cool!) and I am
>> curious as to the  "source" explanation of independent components
>> classified as "channel noise." For the most other classifications, the
>> source explanation is clear to me (e.g., brain~synchronous postsynaptic
>> potentials, muscle~EMG, eye~VEO/HEO/blink movements, etc.), but I am a bit
>> confused as to how one might explain an independent component that
>> reflects
>> a single channel when the channel data itself appears pretty clean? What
>> is
>> the source of the "noise" that's left over in that channel after the
>> projections from other brain and non-brain sources have been subtracted?
>>
>> For peace of mind, the ICA decompositions appear very good and were
>> calculated using "recommended" conditions (i.e., bad channels and bad time
>> segments deleted, average-referenced, 1.0 Hz high-pass filter, full-rank
>> data).
>>
>> Best,
>> Scott
>>
>> --
>> Scott J. Burwell, PhD
>> NIDA T32 Postdoctoral Research Fellow
>> Department of Psychiatry & Behavioral Sciences
>> University of Minnesota, Minneapolis, MN
>> burwell at umn.edu
>> github.com/sjburwell
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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 92093-0961, http://sccn.ucsd.edu/~scott
>


-- 
Scott J. Burwell, PhD
NIDA T32 Postdoctoral Research Fellow
Department of Psychiatry & Behavioral Sciences
University of Minnesota, Minneapolis, MN
burwell at umn.edu
github.com/sjburwell