[Eeglablist] ICLabel: "source" explanation of "channel noise" independent components in the absence of obviously bad data?
Johanna Wagner
joa.wagn at gmail.com
Wed Jan 8 11:32:47 PST 2020
Hi Scott,
the idea of using RV<15% as a cutoff is to use it as an index for the
quality of an IC. Artoni et al, 2014 showed that the RV of a component
dipole is related to the quality of the IC component - thus how stable the
component would be over multiple bootstraps of ICA. The question is if you
would want to include non-reliable components in your analysis... (ICLAbel
does not give you any information on the reliability of a component).
The other option would be to run RELICA on your data to identify reliable,
stable components - I think this is now possible from eeglab with access to
the Neuroscience Gateway (NSG, nsgportal.org) the XSEDE high-performance
computing resources.
This would certainly help to remove some of the ambiguity of ICs in your
ICA solutions...
A general limitation of ICA is the fact that there is only a limited number
of components available to explain the data and thus ICA may not be able to
separate all transient brain processes in a given time period. For this
reason, non-stationary algorithms such as AMICA (Palmer et al., 2007) may
be useful.
Best,
Johanna
Am Mi., 8. Jan. 2020 um 07:58 Uhr schrieb Scott Burwell <burwell at umn.edu>:
> 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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>> mime"
>> >> to
>> >> > > >> eeglablist-request at sccn.ucsd.edu
>> >> > > >>
>> >> > > >
>> >> > > >
>> >> > > > --
>> >> > > > 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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>> >> _______________________________________________
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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
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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
>
--
--
Johanna Wagner, PhD
Postdoctoral Researcher
Swartz Center for Computational Neuroscience
Institute for Neural Computation
University of California San Diego
https://sccn.ucsd.edu/people/#Postdoctoral-Fellows
http://scholar.google.at/citations?user=vSJYGtcAAAAJ&hl=en
<http://sccn.ucsd.edu/>
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