[Eeglablist] ICLabel: "source" explanation of "channel noise" independent components in the absence of obviously bad data?
Scott Burwell
burwell at umn.edu
Wed Jan 8 07:57:53 PST 2020
Dear Johanna and Makoto, thank you both for your replies.
Johanna- thank you for the RELICA reference. I am familiar with the fact
that several published manuscripts have used 15% (e.g., Artoni et al.,
2014) or 5% (Delorme et al., 2012) for choosing "dipolar" components, but I
am not familiar with any reference that has conducted similar analyses at a
wider range of cutoffs, e.g., RV<5% to RV<40% in 5% increments. Under the
RV<15% exclusion criteria, some ICLabeled "Brain" components on the cusp of
RV<15% (e.g., 15.5%) are bound to be dropped, and possibly incorrectly so,
which is not feasible to visually check in big sample sizes like the one I
am working with (n = 1500). Would you (or anyone) happen to know of any
references where a wide range of RV criteria were tested / validated? I was
originally hoping that ICLabel would circumvent having to invoke
assumptions about dipolarity based on dipfit RV%, but it appears that using
some sort of RV% threshold is still in favor.
Makoto- very interesting presentation that you have linked. I especially
like your explanation of the 1^2 inch patch size minimum. I have often
wondered about absolute source sizes / strengths relative to what is
observed at the scalp and your presentation nicely helps to inform this.
(Probably another thread: I'd be curious what this might mean for the
frequencies observed in scalp EEG vs. frequencies observed in ECoG -
presumably the inverse scaling of oscillatory and broadband power over
frequencies suggests that very high frequencies [e.g., high gamma] will be
challenging to measure at the scalp unless the generating patch is very
big.).
Thanks to you both!
Best,
Scott
On Tue, Jan 7, 2020 at 5:17 PM Makoto Miyakoshi <mmiyakoshi at ucsd.edu> wrote:
> 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
> >> > > >> To unsubscribe, send an empty email to
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> >> to
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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
> >> > > To unsubscribe, send an empty email to
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> >> > > For digest mode, send an email with the subject "set digest mime" 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
> >> > _______________________________________________
> >> > Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> >> > To unsubscribe, send an empty email to
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> >> _______________________________________________
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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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--
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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