[Eeglablist] ICLabel: "source" explanation of "channel noise" independent components in the absence of obviously bad data?
Makoto Miyakoshi
mmiyakoshi at ucsd.edu
Tue Jan 7 14:06:29 PST 2020
Dear Scott,
> I think the absolute number of brain components may be closer than the
percentages initially suggest, my 37% of a 61-channel montage (~23
components) versus your 52% of a 40-channel montage (~21 components).
I agree with you. If you are interested, please read my recent thinking
below.
I have been speaking of 'EEG's true degrees of freedom' which is 10-20,
regardless of the number of channels (to certain degrees, of course). See
my recent talk here, I talked about it in the beginning.
https://www.youtube.com/watch?v=lRyZxt2WeKk&t=1256s
When I ran the same simple test on ECoG data, 95% PC was obtained by 78/137
components.
The reason why EEG's true DOF is limited is NOT ONLY because of massive
smearing (i.e., spatial averaging) BUT ALSO scalp-recorded
EEG's requirement that the active cortical patch size must be minimum 1
square inch (Nunez and Srinivasan, 2006; Cooper et al., 1965; Ebersole et
al., 1997). I called this thinking 'electroencephalosophy' which is to
consider the limiting conditions of EEG.
Makoto
On Tue, Jan 7, 2020 at 1:45 PM Scott Burwell <burwell at umn.edu> wrote:
> Thanks for sharing your results, Makoto. They have made me speculate that
> in addition to the noisiness of the data / ICA decomposition, the channel
> number may be important to consider when qualifying the performance of each
> ICLabel class type. I think the absolute number of brain components may be
> closer than the percentages initially suggest, my 37% of a 61-channel
> montage (~23 components) versus your 52% of a 40-channel montage (~21
> components).
>
> It would be interesting to know the ICLabel class breakdown for someone
> using a very dense array system (e.g., 128 or 256 channel montage), would
> we expect more than 20 to 30 "brain sources" that affect the EEG signal and
> can be resolved via ICA?
>
> Scott
>
> On Tue, Jan 7, 2020 at 2:13 PM Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> wrote:
>
>> Dear Scott,
>>
>> > In a large sample of subjects (n = 1500),
>> I have found that the "Other non-brain" class is assigned nearly as often
>> as the "Brain" class, the mean percentages of each classification type
>> being: brain (37%), muscle (13%), eye (5%), heart (0%), line (2%), channel
>> (8%), and other (35%).
>>
>> Interesting. For comparison, let me share my n = 1222 (40 ch) results I
>> presented at Neuroscience2019.
>> See the attached screenshot.
>>
>> Brain 52%
>> Muscle 30%
>> Eye 6%
>> Heart 2%
>> Other 11%
>>
>> Looks like my data (from UCSD Psychiatry, PI Greg Light) has higher rates
>> both brain and muscle.
>>
>> Makoto
>>
>>
>>
>> On Fri, Jan 3, 2020 at 8:43 AM Scott Burwell <burwell at umn.edu> wrote:
>>
>> > Thank you, Luca for the clarification. Indeed, each topography of the
>> > components classified as "Channel Noise" is very focal, so perhaps the
>> > question I should ask is more of the ICA decomposition instead of the
>> > classification.
>> >
>> > I am still a bit curious regarding how to handle components classified
>> as
>> > "Other." Is there any unifying way to describe (and justify removal of)
>> > those components in a manuscript? In a large sample of subjects (n =
>> 1500),
>> > I have found that the "Other non-brain" class is assigned nearly as
>> often
>> > as the "Brain" class, the mean percentages of each classification type
>> > being: brain (37%), muscle (13%), eye (5%), heart (0%), line (2%),
>> channel
>> > (8%), and other (35%). Upon inspection of the "Other" class of
>> components,
>> > the topographies do not look terribly messy or uninterpretable; rather,
>> the
>> > topographies of the "Other" components look less ideal than the "Brain"
>> > components, but I am not certain this justifies excluding these
>> components.
>> > I would like to avoid throwing out more dimensions of the data than
>> what is
>> > needed, and 35% of components (in addition to the ~30% thrown out across
>> > Muscle, Eye, Heart, Line, and Channel Noise) seems like a lot of data.
>> >
>> > I wonder how it would be received by others to only consider a subset of
>> > the columns in the ICLabel classifications output to "force" components
>> > classified as "Other" into one of the other class types? I.e., instead
>> of
>> > considering all ICLabel classes / columns (see below):
>> > EEG = iclabel(EEG);
>> > sourcetypes = [1 2 3 4 5 6 7]; %Considering all ICLabel classes
>> > [~,idx1] =
>> > max(EEG.etc.ic_classification.ICLabel.classifications(:,sourcetypes)');
>> > tabulate(idx1)
>> > Value Count Percent
>> > 1 30 50.85%
>> > 2 11 18.64%
>> > 3 2 3.39%
>> > 4 0 0.00%
>> > 5 0 0.00%
>> > 6 3 5.08%
>> > 7 13 22.03%
>> >
>> > ... consider only a subset of ICLabel classes / columns (1:6), forcing
>> > components above with the classification of "Other" into one of the
>> classes
>> > that is (perhaps) better defined / easier to explain (see below)?
>> > sourcetypes = [1 2 3 4 5 6]; %Considering all but "Other" ICLabel
>> classes
>> > [~,idx2] =
>> > max(EEG.etc.ic_classification.ICLabel.classifications(:,sourcetypes)');
>> > tabulate(idx2)
>> > Value Count Percent
>> > 1 39 66.10%
>> > 2 12 20.34%
>> > 3 3 5.08%
>> > 4 0 0.00%
>> > 5 0 0.00%
>> > 6 5 8.47%
>> >
>> > Curious what you and others think about this approach? And what others
>> have
>> > been doing for cutoffs / class selections with the classification
>> > probabilities?
>> >
>> > Best,
>> > Scott
>> >
>> > Ps. Compliments on the crowd-sourced classifier - it is much needed tool
>> > and very easy to run! I really do appreciate it :)
>> >
>> > On Thu, Jan 2, 2020 at 1:44 PM Luca B Pion-tonachini <
>> > lpiontonachini at ucsd.edu> wrote:
>> >
>> > > Hi Scott (and Scott),
>> > >
>> > >
>> > >
>> > > The principle factor for determining a “Channel Noise” IC is the scalp
>> > > topography. If the topography is very focal, that is often an
>> indication
>> > of
>> > > a “Channel Noise” component. All that really means, is that the
>> channel
>> > > described by the IC is in some way already independent of the other
>> > > channels prior to ICA decomposition. If the components ICLabel marked
>> as
>> > > “Channel Noise” look very focal (you could double check this by
>> looking
>> > at
>> > > the corresponding columns of the EEG.icawinv matrix: one element of
>> each
>> > of
>> > > those columns should have much higher magnitude), then ICLabel is
>> > labeling
>> > > the ICs correctly and the real question is “why did ICA decompose
>> those
>> > > components that way?” If the components don’t actually look like they
>> > > describe channel noise, then it is likely that ICLabel is wrong (I
>> > > personally hope not, but it does happen), in which case you could
>> state
>> > > that as the likely explanation for the classification.
>> > >
>> > >
>> > >
>> > > Luca
>> > >
>> > >
>> > >
>> > >
>> > >
>> > > *From: *Scott Burwell <burwell at umn.edu>
>> > > *Sent: *Thursday, January 2, 2020 12:04 AM
>> > > *To: *Scott Makeig <smakeig at ucsd.edu>
>> > > *Cc: *eeglablist at sccn.ucsd.edu
>> > > *Subject: *Re: [Eeglablist] ICLabel: "source" explanation of "channel
>> > > noise" independent components in the absence of obviously bad data?
>> > >
>> > >
>> > > 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
>> > > >> _______________________________________________
>> > > >> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
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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
>> > > _______________________________________________
>> > > Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
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>> > >
>> >
>> >
>> > --
>> > 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
>> > _______________________________________________
>> > Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>> > To unsubscribe, send an empty email to
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>
>
>
> --
> 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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