[Eeglablist] ICLabel: "source" explanation of "channel noise" independent components in the absence of obviously bad data?
Scott Makeig
smakeig at ucsd.edu
Thu Jan 23 12:37:34 PST 2020
Scott,
You might want to actually assess the summed ERP (if relevant) of your
rejected 'Other' ICs - do they make, altogether, a significant (ERP)
contribution??
As for 'Single Channel' ICs, looking at their erpimage (in an 'IC
properties panel' (especially Luca's from ICLabel tools) will
typically show some period in the experiment during which the channel 'went
wild' .
If a serious concern, one might interpolate this channel only during that
(if brief) period. Doing this for the the applicable Channel Noise ICs
could make the data eligible for a second decomposition (in which the
additional channel data benefit would weigh against the rank reduction
during the brief interpolated portions.... This might be a good 'numerical
experiment' to perform... Do the resulting ICs differ much from the 1st
decomposition??
However, if the removed channels contribute ~0 to the measure of interest
(e.g., ERP or ?), then removing them has ~no effect on your results (except
to reduce data 'noise' somewhat)....
Scott Makeig
On Fri, Jan 10, 2020 at 7:46 AM Scott Burwell <burwell at umn.edu> wrote:
> Thank you for the further clarification, Johanna. I will look into running
> RELICA on my data to further assess components' reliability. As you note,
> transient effective EEG processes may give rise to spurious components in
> the ICA decomposition. I am using AMICA with some of the artifact options
> enabled to reject "unlikely data," but still only estimating one AMICA
> model, so perhaps estimation of more than one model is desirable to suss
> out bad components.
>
> Best,
> Scott
>
> On Wed, Jan 8, 2020 at 1:32 PM Johanna Wagner <joa.wagn at gmail.com> wrote:
>
>> 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
>>>> >> > > >> eeglablist-unsubscribe at sccn.ucsd.edu
>>>> >> > > >> For digest mode, send an email with the subject "set digest
>>>> 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
>>>> >> > > To unsubscribe, send an empty email to
>>>> >> > > eeglablist-unsubscribe at sccn.ucsd.edu
>>>> >> > > For digest mode, send an email with the subject "set digest
>>>> mime" to
>>>> >> > > eeglablist-request at sccn.ucsd.edu
>>>> >> > >
>>>> >> >
>>>> >> >
>>>> >> > --
>>>> >> > 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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>>>> >> _______________________________________________
>>>> >> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>>>> >> To unsubscribe, send an empty email to
>>>> >> eeglablist-unsubscribe at sccn.ucsd.edu
>>>> >> For digest mode, send an email with the subject "set digest mime" to
>>>> >> eeglablist-request at sccn.ucsd.edu
>>>> >
>>>> >
>>>> >
>>>> > --
>>>> > 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
>>>
>>
>>
>> --
>>
>> --
>> 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/>
>>
>
>
> --
> 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
>
--
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
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