[Eeglablist] paper published

Fri Dec 9 20:06:18 PST 2016

Dear Rob,

Thank you for sharing your nice studies. Yes, when I met Iman, he mentioned
these papers to me, and we discussed SIFT.
SIFT does not come with full-fledged group-level solution. Recently, I
presented the related methods in SfN and EEGLAB workshop. For your interest
I put URL links to info.
https://sccn.ucsd.edu/mediawiki/images/1/18/Group-Level_Connectivity.pdf
https://sccn.ucsd.edu/mediawiki/images/4/41/MakotoSfN2016.pdf

Makoto

On Wed, Dec 7, 2016 at 8:21 PM, Rob Coben <drcoben at gmail.com> wrote:

> I would agree as well and am pleased to see this paper and scott’s as
> well. This accumulation of knowledge and application of these techniques is
> critical. For those interested we have presented these approaches in two
> papers and an upcoming book chapter as well:
>
>
>
> http://journal.frontiersin.org/article/10.3389/fnhum.2014.00045/full
>
>
>
> http://journal.frontiersin.org/article/10.3389/fnhum.2015.00194/full
>
>
>
> Rob
>
>
>
>
>
> *From:* eeglablist-bounces at sccn.ucsd.edu [mailto:eeglablist-bounces at scc
> n.ucsd.edu] *On Behalf Of *Makoto Miyakoshi
> *Sent:* Wednesday, December 7, 2016 8:49 PM
> *To:* Frederik Van de Steen <Frederik.VandeSteen at ugent.be>
> *Cc:* eeglablist at sccn.ucsd.edu
> *Subject:* Re: [Eeglablist] paper published
>
>
>
> Dear Frederik,
>
>
>
> It's such an honor to receive a kind explanation from the first author of
> the critical paper. Thank you.
>
>
>
> I read your comment but did not see the answer. I read discussion section
> of your paper and found the following description.
>
>
>
> > However, since the mixing of sources is not identical for the different
> channels, the inclusion of past values of another (driver) channel can
> improve the prediction of another (target) channel’s current state.
>
>
>
> Yeah, this makes sense... I confused sources with channels. After applying
> MVAR on channel signals, of course the result is across-channel
> connectivity... who wants to know it.
>
>
>
> However, this means that preprocessing methods to obtain source EEG
> activity is critical. The author of SIFT, Tim Mullen, participated to write
> this paper, which claims that it does better job than AMICA.
>
> https://www.hindawi.com/journals/cin/2016/9754813/
>
> My point is that if the selection of this kind of preprocessing methods
> and algorithms can determine the basic quality of the connectivity
> ananlysis, then the EEG connectivity analysis itself if very much dependent
> on its source-decomposing preprocessing. I kind of knew it, but your
> publication, together with Clemens's, proved it. This may promote the use
> of ICA actually.
>
>
>
> Anyway, thank you very much Frederik. I understood it now.
>
>
>
> Makoto
>
>
>
>
>
>
>
> On Wed, Dec 7, 2016 at 3:22 AM, Frederik Van de Steen <
> Frederik.VandeSteen at ugent.be> wrote:
>
> Dear Makoto,
>
>
>
> We investigated this issue in our recent paper:
>
> Van de Steen, F., Faes, L., Karahan, E., Songsiri, J., Valdes-Sosa, P. A.,
> & Marinazzo, D. (2016). Critical comments on EEG sensor space dynamical
> connectivity analysis. *Brain Topography*, 1-12.
>
> This work was conducted independently from and simultaneously as Brunner et
> al (2016). Both papers show how spurious connectivity on sensor space
> analysis can, in many cases, occur (not always though).
>
> I'll try my very best to make it intuitively:
>
> first of all, you should be aware that non negative (d)DTF, (r)PDC, time
> domain granger causality etc. all imply at least one non-negative off
> diagonal coefficient of the coefficient matrix (*B*) of the MVAR model.
>
> To clarify: time series are modelled with a multivariate
> autoregressive model (MVAR) in which current values of time series are
> explained by a linear combination of past values of that time series itself
> and past values of other time series: X(t)= *B*1*X(t-1) + *B*2*X(t-2) +
> etc+ error where X(t) is a column vector contain the current values of all
> time series (X(t) = [X1(t) X2(t) ...]T). Diagonal elements of *B* relate
> past values (t-1, t-2 etc) of a time series to the current value (t) of the
> time series itself (e.g X1(t-1) to X1(t) )). Off diagonal elements relate
> past values of another time series to the current value (e.g X2(t-1) to
> X1(t)))
>
> secondly, two independent time series ≠ two independent time series. what
> i mean is that there are different ways in which time series can be
> independent of one another. e.g two white noise processes
> vs. two independent autoregressive time series. In the former case the time
> series cannot be modelled with past values  of another time series nor can
> it be modelled with past values of that time series itself (all elements in
> *B* are zero). In the latter case, the situation is different. There you
> cannot model one time series with past values of the other but you can
> model it with past values of the time series itself (i.e. only non zero
> elements on diagonal of *B*). Now when you mix up (linear superposition)
> these time series (by volume conduction), the past contains information of
> both original time series.
>
> since the mixing of time series is not identical (i.e. each EEG channel is
> a unique linear combination of sources), the inclusion of past values of
> another mixed time series can improve the prediction of the other mixed
> time series. This translates, in terms of the MVAR model, in a (some)
> non-negative off diagonal elements in *B* and thus also non negative DTF,
> PDC, etc. When mixing up white noise, no spurious connectivity will occur
> because the past wasn't informative in the first place.
>
> You need to be aware that even though volume conduction implies
> instantaneously mixing of time series, the continuation of time does not
> unmix the past...
>
> So basically these measures are not insensitive to volume conduction as
> claimed by some.
>
> Hope it is a bit clear. Please do not hesitate to ask more questions
>
>
>
> Kind regards,
>
>
>
> Frederik
>
>
> ------------------------------
>
> *Van:* Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> *Verzonden:* woensdag 7 december 2016 03:46
> *Aan:* Scott Makeig
> *CC:* eeglablist at sccn.ucsd.edu
> *Onderwerp:* Re: [Eeglablist] paper published
>
>
>
> Dear Scott and list,
>
>
>
> During the EEGLAB Workshop 2016, I asked this question to Tim Mullen
> during his lecture, without knowing this ongoing debate. Intuitively, it
> does not make sense to me why just linear mixing affects connectivity
> calculation, if dDTF or rPDC can suppress the spurious connections... Tim
> mentioned that there is good reason for this, but did not explain it during
> the lecture due to limited time. Can anyone give me an intuitive
> explanation why it is bad?
>
>
>
> Makoto
>
>
>
>
>
>
>
> On Wed, Nov 30, 2016 at 6:02 PM, Scott Makeig <smakeig at ucsd.edu> wrote:
>
> Some of us noticed a paper published recently that claimed that effective
> connectivity measures between scalp EEG channels suffer no ill effects of
> volume conduction -- and immediately questioned its conclusions!  Clement
> Brunner of Graz mounted an effort to publish a rebuttal in the same
> journal, which has now appeared:
>
>
>
> C. Brunner, M. Billinger, M. Seeber, T.R. Mullen, S Makeig, Volume
> conduction influences scalp-based connectivity estimates
> <https://sccn.ucsd.edu/~scott/pdf/brunner16.pdf>(a rebuttal). *Frontiers
> in Computational Neuroscience*,
>
> doi:10.3389/fncom.2016.00121, 22 November 2016.
>
>
>
> We have learned that another group is publishing a separate rebuttal ...
>
>
>
> Scott Makeig
>
>
>
> --
>
> 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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>
>
>
> --
>
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
>
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>
>
> --
>
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>

-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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