[Eeglablist] paper published

Wed Dec 7 18:48:37 PST 2016

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
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