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<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif">Pedro,</span></p>
<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif"> Although
my comments were not about lagged coherence and instead about the phase slope
index and distortion of phase differences by ICA reconstruction I agree that
the volume conductor that contains heterogeneous tissue compartments can also alter
time or phase differences. However, the
fact is that the magnitude of effect on phase differences by a heterogeneous
volume is small (e.g., a few milliseconds or 1 to 2
degrees) compared to physiological factors such as white matter conduction
velocity which is as much as 40 degrees at Euclidean distances of 120
millimeters between Brodmann area center voxels. Here is a url to a publication showing this,
see figure 6 at:</span></p>
<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif">http://www.appliedneuroscience.com/Default_Network_LORETA_Phase_Reset-Thatcher_et_al.pdf</span></p>
<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif"> </span></p>
<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif">Also, one can correlate phase differences with white
matter fiber spacing such as the ‘U’ shaped cortico-cortical white matter at
the scalp surface. If one transforms
phase differences into milliseconds then as one increases scalp interelectrode
distance phase differences vary from 5 msec for closely spaced electrodes to
over 100 msec for Fp1/2-O1/2 spacings. Here
is a url to a publication showing this, see figures 7 & 8 at:</span></p>
<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif">http://www.appliedneuroscience.com/TWO-COMPARTMENTAL_MODEL_EEG_COHERENCE.pdf</span></p>
<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif"> </span></p>
<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif">What is more important is the false assumption that
ICA reconstruction some how corrects for a heterogeneous volume conductor when
there is no evidence to support this claim.
Further the correlations with underlying physiological factors such as synaptic
delays and white matter conduction velocities are obliterated by ICA reconstruction. Therefore the effects of a heterogeneous
tissue volume conductor are also distorted by ICA reconstruction. For example,
once phase differences in the original time series are altered by ICA
reconstruction for each and every time point then covariance calculations are
no longer valid since the primary level of data has been altered. Although I did not mention lagged coherence,
I just compared lagged coherence between the original EEG recording and the
Australian and Delorme ICA Reconstructions (i.e., using 17 ICs to create 19
channels) and 100% of the values were different and statistically all were greater
than 0.0000001 different. The argument on
this list server is that there is no ground truth of the EEG and that the original
EEG is nothing but noise and artifact and therefore it is ok to alter all of the phase differences in the original EEG recording. In my opinion and the opinion of many others (not on the Eeglablist) once the phase differences in the original recording are altered then all subsequent network analyses including lagged coherence, directed
coherence and phase slope index are decoupled from the physiological ground truth of the EEG. </span></p>
<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif"> </span></p>
<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif">As always it is great to hear from you!</span></p>
<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif"> </span></p>
<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif">Warm regards,</span></p>
<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif"> </span></p>
<p class="ydp82280dc9MsoNormal"><span style="font-size:12.0pt;line-height:107%;font-family:"Times New Roman",serif">Bob</span></p>
<!--EndFragment--></div></div><div><br></div><div><br></div><div id="yahoo_quoted_3094813971" class="yahoo_quoted"><div style="font-family:'Helvetica Neue', Helvetica, Arial, sans-serif;font-size:13px;color:#26282a;"><div>On Sunday, August 6, 2017, 3:40:07 PM EDT, Pedro Antonio Valdés-Sosa <pedro.valdes@neuroinformatics-collaboratory.org> wrote:</div><div><br></div><div><br></div><div><div id="yiv9734210705"><html><head><style>#yiv9734210705 #yiv9734210705 --
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<p class="yiv9734210705MsoNormal">Great Summary of EEG generation RobertI</p>
<p class="yiv9734210705MsoNormal">I agree that there is great confusion regarding instantaneous effects of volume conduction and phase relations.</p>
<p class="yiv9734210705MsoNormal">I must warn that the volume conductor model is not only instantaneous. It affects all the elements used to calculate phases at the electrodes.</p>
<p class="yiv9734210705MsoNormal">I reproduce a summary that Thomas Koenig and I are carrying out in the form of the usual phase fallacy arguments (in black) and a a criticism (in red)</p>
<p class="yiv9734210705MsoNormal"> </p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:black;">1: Volume conduction leads to instantaneous correlations on the scalp (correct).</span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:red;">CORRECT</span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:black;"> </span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:red;">BUT it also induces mixing of lagged interactions</span><span style="color:black;"></span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:red;">V(t)=K j(t) + e(t), V voltage, K lead field, J currents, e sensor noise. Everything is indexed by time.</span><span style="color:black;"></span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:red;">Lets look at covariance since correlation and coherence—and phase as a consequence-- comes from it. Assuming stationarity (without loss of generality).</span><span style="color:black;"></span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:red;">SigmaV(0) = K SigmaJ(0) K’ + sigmae^2 K’ is lead field transposed</span><span style="color:black;"></span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:red;">SigmaV (0) covariance of EEG at lag 0—that is cov [V(t) with V(t)], SigmaJ(0) idem for sources, sigmae^2 sensor noise variance</span><span style="color:black;"></span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:red;">Now let look at lagged covariance SigmaV(lag) that it cov(V(t), V(t=tau))</span><span style="color:black;"></span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:red;">Assuming sensor white noise the sigmae^2 disappears</span><span style="color:black;"></span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:red;">SigmaV(tau)= K SigmaJ(tau) K’</span><span style="color:black;"></span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:red;">Where is the disappearance of the effect of volume conduction persists even on lagged influences?</span><span style="color:black;"></span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:black;"> </span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:black;">2: by using phase-lagged quantifiers of dependencies among pairs of channels, we can eliminate the effect of instantaneous correlations that are potentially explained by volume conduction
in the given pair of channels.</span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:red;">FALSE because of the argument above this is totally false and is the phase fallacy</span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:red;">Volume condution, modeled by the lead field affects lagged measures as much as instantaneous ones</span><span style="color:black;"></span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:black;"> </span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:black;">3: Thus, we have completely eliminated the volume conduction problem.</span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:red;">FALSE since 2 is not true</span><span style="color:black;"></span></p>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="color:black;"> </span></p>
<p class="yiv9734210705MsoNormal"><span style="color:red;">There are a number of measures that purport to avoid volume conduction effects, both instantaneous and lagged and allow inference of brain interactions at the scalp. These measures are “magical” since they solve
the effect of the Lead field without factoring it into the model.</span><span style="color:red;"></span></p>
<p class="yiv9734210705MsoNormal"> </p>
<p class="yiv9734210705MsoNormal"> </p>
<div class="yiv9734210705yqt5490856464" id="yiv9734210705yqt92109"><div style="border:none;border-top:solid #B5C4DF 1.0pt;padding:3.0pt 0cm 0cm 0cm;">
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><b><span style="font-size:12.0pt;color:black;">From:
</span></b><span style="font-size:12.0pt;color:black;">eeglablist <eeglablist-bounces@sccn.ucsd.edu> on behalf of Robert Thatcher <rwthatcher2@yahoo.com><br clear="none">
<b>Date: </b>Sunday, 6 August 2017 at 15:23<br clear="none">
<b>To: </b>Marius Klug <marius.s.klug@gmail.com>, "tarikbelbahar@gmail.com" <tarikbelbahar@gmail.com><br clear="none">
<b>Cc: </b>Eeglablist <eeglablist@sccn.ucsd.edu><br clear="none">
<b>Subject: </b>Re: [Eeglablist] FW: Beyond good and evil of ICA</span></p>
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<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"> </p>
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<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;">Marius,</span><span style="font-size:12.0pt;"></span></p>
<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;"> The ground truth of the EEG and phase delays begins with the physics of the electrical field potential (<span style="color:#222222;">or
the electrostatic </span>potential) defined as the amount of work needed to move a unit positive charge from a reference point to a specific point inside the field without producing any acceleration. When the </span><span style="font-size:12.0pt;"><a rel="nofollow" shape="rect" target="_blank" href="https://en.wikipedia.org/wiki/Curl_(mathematics)" title="Curl (mathematics)"><span style="color:#0B0080;">curl</span></a><span style="color:#222222;"> </span></span><span class="yiv9734210705ydp77d16cb1nowrap"><span style="font-size:12.0pt;">∇</span></span><span class="yiv9734210705ydp77d16cb1nowrap"><span style="font-size:12.0pt;"> × E</span></span><span style="font-size:12.0pt;"> is
zero then the electrical potential is determined by the gradient of the electrical field. EEG differential amplifiers are used to measure the difference or gradient of the electrical field and therefore the electrical potential is the difference between the
two inputs to the amplifier. If one measures the electrical field and then removes ICs and starts with a smaller number of EEG channels to create a larger number of time series to replace the original time series then this changes the gradients of the electrical
field produced by the brain and also distorts the time differences between samples then the physics of the electrical field no longer applies.</span><span style="font-size:12.0pt;"></span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;">The majority of neuroscientists agree that the physiological ground truth for the genesis of the EEG as being summated synaptic potentials in
groups of neurons that are connected by axons. The ground truth of phase differences between separated scalp locations are physiological factors of the electrical sources and the connections between sources (i.e., networks) such as axonal conduction velocities,
length of connecting axons, synaptic rise times, synaptic delays and synaptic integration times, etc. This is well established science and it was quite surprising to read posts on this forum by individuals that claim that there is no ground truth of the
EEG, EEG is all noise and therefore this justified altering phase differences using a small number of ICs to create different time series (i.e., reconstruction) to thereby decoupling the brain from the subject and the physics of electrical fields. Like Pedro
Valdez I have also recorded EEG from the dura and pia surface and simultaneously from the scalp. I also have impaled neurons and correlated the intracellular EEG with field potentials as a function of distance from the impaled neurons. These recordings were
when I was a post doc at Albert Einstein College of Medicine With E. Roy John we moved multiple micro-electrodes through wide regions of the cat brain while also recording EEG from the scalp. It was during this time that Roy and myself and others quantified
how phase differences increase or decrease as a function of interelectrode distance. At the University of Maryland myself and colleaques, while still collaborating with Roy, did extensive studies that linked phase differences to the cortical white matter
with short distance ‘U’ shaped fibers vs long distance fasciculi. These measures helped Paul Nunez to use scalp EEG phase differences to estimate white matter conduction velocities. Additional validation studies of the network physiological ground truth
of EEG phase differences was when I worked at NIH with patients before and after craniotomy. Further validations also involved correlations with MRI T2 relaxation time. </span><span style="font-size:12.0pt;"></span></p>
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<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;"> </span></p>
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<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;">Luckily ICA reconstruction was not invented in the 1960s because if it had then today we would have no idea that there is a replicable and verifiable
ground truth of EEG nor know about the relationship between EEG phase differences and the cerebral white matter.
</span><span style="font-size:12.0pt;"></span></p>
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<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;"> </span><span style="font-size:12.0pt;"></span></p>
<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;">As for volume conduction and phase delays between separated sensors I think that there may be some confusion between the instantaneous
physics of the electrical potential and the volume conductor model used for the inverse solution. The volume conductor model pertains to a single instant of time where the accuracy of the spatial localization of sources is affected by inhomogeneities of tissue
conductivities that primarily affect amplitudes and only negligibly time differences which are near to the speed of light. A good book on this topic is Malmuvo and Plonsey “Bioelectricity”, Oxford University, 1995 or a standard physics text book, for example,
Richard Feynman’s book. Here is a url to a study where we evaluated phase differences as a function of the Euclidean distance between Brodmann areas that comprise the default network - see figure 6 at:</span><span style="font-size:12.0pt;"></span></p>
<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;">http://www.appliedneuroscience.com/Default_Network_LORETA_Phase_Reset-Thatcher_et_al.pdf</span><span style="font-size:12.0pt;"></span></p>
<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;"> </span><span style="font-size:12.0pt;"></span></p>
<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;">We have also evaluated the distortion of effective connectivity by ICA reconstruction. For example, we computed the phase slope index
before and after ICA reconstruction and 100% of the effective connectivity between 171 electrode pairs for all frequencies is adulterated. This is another example of decoupling the “ground truth” of the physiology of the brain from the subject. We recently
published a study that correlated intelligence with effective connectivity where we found significant differences between low and high I.Q. subjects. If we had used ICA reconstruction then because phase differences are scrambled and distorted and thereby
decoupled from the brain it is unlikely that there would have been any significant differences.
</span><span style="font-size:12.0pt;"></span></p>
<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;">Here is a url to this study: http://www.appliedneuroscience.com/Intelligence%20&%20information%20flow-Thatcher%20et%20al%202016.pdf</span><span style="font-size:12.0pt;"></span></p>
<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;"> </span><span style="font-size:12.0pt;"></span></p>
<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;">For those interested, here is a url to a book that E. Roy John and myself published in 1977 that reviews the history of how the ground
truth of EEG was discovered. The ICA replacement violates the electricity and physiology of the brain and contradicts this history: http://www.appliedneuroscience.com/FuncNeuro_v1n.pdf</span><span style="font-size:12.0pt;"></span></p>
<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;"> </span><span style="font-size:12.0pt;"></span></p>
<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;">Best wishes,</span><span style="font-size:12.0pt;"></span></p>
<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;"> </span><span style="font-size:12.0pt;"></span></p>
<p class="yiv9734210705ydp77d16cb1msonormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;">Robert</span><span style="font-size:12.0pt;"></span></p>
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<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;"> </span></p>
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<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:12.0pt;"> </span></p>
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<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">On Friday, August 4, 2017, 1:10:52 PM EDT, Marius Klug <marius.s.klug@gmail.com> wrote:</span></p>
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<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
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<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
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<p class="yiv9734210705MsoNormal" style="margin-right:0cm;margin-bottom:12.0pt;margin-left:36.0pt;">
<span style="font-size:10.0pt;">Hi everyone!</span></p>
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<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">One thing after the other...:</span></p>
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<span style="font-size:10.0pt;"> </span></p>
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<p class="yiv9734210705MsoNormal" style="margin-right:0cm;margin-bottom:12.0pt;margin-left:36.0pt;">
<span style="font-size:10.0pt;">Georges, sure, feel free to use the example, I'm always happy to contribute :-)
</span></p>
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<p class="yiv9734210705MsoNormal" style="margin-right:0cm;margin-bottom:12.0pt;margin-left:36.0pt;">
<span style="font-size:10.0pt;">A comparison of high to low density ICA cleaning would definitely be interesting and my hypothesis is that the ICA decomposition might be significantly worse (meaning more
changes in the posterior channels) in the 19-channels set. If I have some spare time I might do that and post the results. Will take a while, though, I won't have time until end of September I think...<br clear="none">
<br clear="none">
</span></p>
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<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
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<p class="yiv9734210705MsoNormal" style="margin-right:0cm;margin-bottom:12.0pt;margin-left:36.0pt;">
<span style="font-size:10.0pt;">Robert, if you read again my very first email on the topic, I have not changed my mind the slightest bit... I have said from the beginning that ICA does indeed change the data,
which is rather obvious. It's only about the question where this change is a positive or a negative one. However, as I said, it might be that 19 channels is not a sufficiently high number for a decent ICA decomposition for cleaning. It sure is not for working
on the source level, but by now I did indeed assume that the eye components are so strong that even 19 channels are enough to cleanly take them out. If that's not the case, it's an important information and everyone needs to decide for themselves if they want
to use ICA and risk losing a slight bit of brain data or other cleaning methods which might prove to be better in the low-density segment. I would, however still HIGHLY encourage you to test whether or not the results with the ICA-based cleaning are significantly
better or worse than without. As long as you have not tested this, you keep rejecting a method that might prove useful for you.</span></p>
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<p class="yiv9734210705MsoNormal" style="margin-right:0cm;margin-bottom:12.0pt;margin-left:36.0pt;">
<span style="font-size:10.0pt;">The as you call it false belief of continuous artifacts has in my opinion already been adressed enough and I guess you just must have special means of keeping eye and neck
muscles and heart beats from continuous signals in the EEG, since in ordinary participants eye components seem to correlate highly with eye tracker signals and I can still see the heart beats of my subjects in their EEG. As I've stated elsewhere, there are
many means of increasing signal-to-noise ratio, ICA just being one of them (one other would for example be the averaging of ERPs). Many other published sources just tell us that those other means work as well and that the signal had been strong enough for
statistically significant results. It does not say there are no continuous artifacts, that's a logical fallacy.</span></p>
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<p class="yiv9734210705MsoNormal" style="margin-right:0cm;margin-bottom:12.0pt;margin-left:36.0pt;">
<span style="font-size:10.0pt;">As for the statistically independent sources in ICA and connected brain regions: This is an interesting thing to think about, that's true. One answer would be that ICA has
no temporal knowledge while separating the sources, it just works with point clouds, but the brain connections are temporal. One other might be that ICA doesn't separate based on correlation but more complex measures of the data set. I must admit I didn't
have the time yet to fully understand the different ICA algorithms, though. My last answer to this is the fact that ICA gets nicely dipolar components which can be projected back to the brain, and this also works in simulations where the true sources are known.
So for me, at least with sufficiently high density (64 at least, better 128) ICA components that have a neat separation from other components are brain sources. This has to be taken with a grain of salt though, since one needs to be experienced in understanding
inhowfar a component might still be mixed sources. For example residual variance of the component gives at least a hint about this question. Unless proven otherwise, I will keep my working definition of ICA components as sources, and especially dipolar sources
as brain sources, since I work on the source level anyway... </span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-right:0cm;margin-bottom:12.0pt;margin-left:36.0pt;">
<span style="font-size:10.0pt;">Be it as it may, the 18-19 bitcoin example still doesn't hold true, you can just replace "sources" with "components". You made another strawman argument which I answered because
it's an interesting question, not because it's an actual counter argument against my case.</span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">Also, I think exactly the central-limit theorem is being used in ICA. A singular source is always less gaussian than mixed sources,
so a de-mixing matrix which maximizes non-gaussianity of components would maximize the separation of the components from each other. I think your argument here goes exactly where the basic ICA argument goes so I don't see your point. See
<a rel="nofollow" shape="rect" target="_blank" href="http://arnauddelorme.com/ica_for_dummies/">http://arnauddelorme.com/ica_for_dummies/</a></span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-right:0cm;margin-bottom:12.0pt;margin-left:36.0pt;">
<span style="font-size:10.0pt;"><br clear="none">
<br clear="none">
</span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-right:0cm;margin-bottom:12.0pt;margin-left:36.0pt;">
<span style="font-size:10.0pt;">Makoto, yes, the dipole model is definitely far from perfect. We also attempt to create a head model including the eyes and neck muscles, since the head movements are vitally
important for us. Blood vessels is an interesting point though, we hadn't thought about this, as far as I know at least. This would definitely improve localization of the muscle artifacts and simplify the discrimination of eye and muscle, but of course muscles
are complex in their electrical properties so it's going to be an interesting question if this is working as intended. The distributed source model does appeal to me in the sense that it's just more realistic than a single dipole creating the signal at the
scalp, but I am not familiar with the assumptions that are held for this.<br clear="none">
<br clear="none">
Another big issue to me is that we don't get the same components for each subject. Clustering is a really bad way of combining the components, even though it's the best we have. In fact, I've realized that the clustering solution varies to a more-than-slight
degree even if you re-cluster with the exact same parameters. I've thought of a method to do repeated clustering and compare the results to have a kind of probabilistic model slightly similar to Nima's Measure Projection, but it has proven to be more work
than I had time. I might come back to it, though! As you said, a long way to go to make EEG a smoothly working brain imaging technique.. but all the more interesting and it's fascinating to be part of this!<br clear="none">
<br clear="none">
<br clear="none">
</span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-right:0cm;margin-bottom:12.0pt;margin-left:36.0pt;">
<span style="font-size:10.0pt;">Georges, I don't think anyone ever doubted that the data changes after one takes out eye components. It's obvious and I don't recall emails that state otherwise. The whole
discussion was about the fact that the change in the data would increase the quality and make it closer to the actual brain data, that was even in Arno's very first post. It's reflected in the wording, for example you and robert use "distortion" or "adulteration",
while Arno, Makoto and others from this ist use simply "change", because this implies no negative impact.</span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-right:0cm;margin-bottom:12.0pt;margin-left:36.0pt;">
<span style="font-size:10.0pt;">I think an important thing to keep in mind is that we can not really make any meaningful conclusions from sensor level activity as to where this activity originated from. Sensor-level
connectivity will always have this drawback, plus the mixing in of artifactual sources, which is why Johanna has said a while ago that she performs only high-density EEG source-level connectivity analysis, nothing on the sensor-level at all. It obviously depends
on your data set and application, but to me, 19 channel surface EEG is always to be treated with extra care as to any interpretations.<br clear="none">
<br clear="none">
<br clear="none">
</span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-right:0cm;margin-bottom:12.0pt;margin-left:36.0pt;">
<span style="font-size:10.0pt;">Tarik, yes, it seems to me that there is tons of method research to be done and paper to be written. We soon will have a synchronized data set containing high-density EEG with
28 as EMG on the neck, together with motion capture of the head, shoulders, hands and feet in position and orientation and eye tracking while moving around in virtual reality. This might provide useful for investigating the continuous artifacts, since we have
eye movement, muscle activity, and (if we record this in addition) ECG all synchronized and we can look for it's connections to the EEG data. Well, the things to come! It all needs time and resources... Anyways, I would be glad to contribute to some of the
papers if I can, since investigating the EEG methods is one of my main interests as a researcher.<br clear="none">
<br clear="none">
</span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">All the best,</span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">Marius</span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
<div>
<div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">2017-07-28 20:23 GMT+02:00 Tarik S Bel-Bahar <<a rel="nofollow" shape="rect" ymailto="mailto:tarikbelbahar@gmail.com" target="_blank" href="mailto:tarikbelbahar@gmail.com">tarikbelbahar@gmail.com</a>>:</span></p>
<blockquote style="border:none;border-left:solid #CCCCCC 1.0pt;padding:0cm 0cm 0cm 6.0pt;margin-left:4.8pt;margin-right:0cm;">
<div id="yiv9734210705ydpbeb3a7e6yiv0625967697yqt77448">
<div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">Greetings, it's great to see a more congenial future-focused discussion
</span><span style="font-size:10.0pt;"></span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">building</span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> here.
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> We all have knowledge we consider as true, biases that impact our perceptions and expectations, and this all sometimes gets
in the way of (needed) discussion on research topics and assumptions. It seems we often need less "fixed ideas and assumptions" and to be more open to "new published data-based findings addressing specific topics". </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">Overall, kudos for keeping the
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">human </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">conversation going
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">, it may eventually lead to positive impacts for the EEG field.</span></p>
</div>
</div>
<div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">From my perspective,</span><span style="font-size:10.0pt;">
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">argument
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">s</span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">or point of view, whether based on theory
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">, math,</span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">or experience,</span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">will
</span><span style="font-size:10.0pt;"></span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">remain</span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">un</span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">satisfactory until th
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">ey</span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> are
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">in 1) </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">multiple published new high-quality
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> empirical</span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">papers
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">that 2) examine a </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">range of topics related to the discussion here about ICA, EEG,
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">phase metrics, </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">artifacts, etc... from a
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">3) </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">data-and assessment-focused point of view.
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">It's important to have conversations, but it's new peer-reviewed publications that will truly clarify things.</span></p>
</div>
</div>
<div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
</div>
<div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">I may be too optimistic, but more collaboration amongst the discussants here could generate a half-dozen papers over the next
year or two focused on these issues. These are opportunities for both young flexible researchers, as well as more mature researchers and clinicians. </span></p>
</div>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">One thing that has struck me is that the conversation has reflected
</span><span style="font-size:10.0pt;"></span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">1) </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">a divide between low-density and high-density EEG systems users,
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">2) the differences in researcher and clinician values, </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">and
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">that 3) </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">there is enough data
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">already </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">out there to provide data-based publications addressing these topics. Further, the I think there is a lot of room for theoretical
and methodological consolidation in the EEG field, but this will take time and effort, just as a clear understanding of the nature of true neural sources will take time and effort. </span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">Below, a resend of my earlier list of topics that future articles can consider</span><span style="font-size:10.0pt;">
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> or that we can add to/clarify</span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">, and a list of articles (and authors) that
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">can</span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> be consulted regarding current use/opinions of ICA, EEG, phase, and con
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">n</span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">ectivity
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> analyses</span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">.</span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><b><span style="font-size:10.0pt;">Possible dimensions/constraints to consider or compare?</span></b><span style="font-size:10.0pt;"></span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"><br clear="none">
Channel density and relative total-head coverage</span><span style="font-size:10.0pt;">
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
<div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">Low-density vs. moderate-density vs high-density</span><span style="font-size:10.0pt;"></span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">With and without EOG and/or EMG recording ?</span><span style="font-size:10.0pt;"></span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"><br clear="none">
Number/type of artifact</span><span style="font-size:10.0pt;">
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">ual detected, number/type of</span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">ICs removed
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
<div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">IC-based vs. non-IC based artifact removal</span><span style="font-size:10.0pt;"></span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">Number/type of artifact visible in channel record</span><span style="font-size:10.0pt;">
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"></span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"><br clear="none">
<br clear="none">
Clarity/robustness of artifact </span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">ual ICs </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">(e.g., ICs that are mixed vs. ICs that<br clear="none">
are mixed (containing both artifact and neural info)<br clear="none">
<br clear="none">
Channel-level vs. ICA-level vs. source </span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">-estimated</span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> connectivity metrics<br clear="none">
<br clear="none">
Length of epochs/trials<br clear="none">
<br clear="none">
Type of source analysis<br clear="none">
<br clear="none">
Type of reference (e.g., Chella et al., 2016)<br clear="none">
<br clear="none">
Type of ICA/blind source separation (e.g., Bridwell et al., 2016;<br clear="none">
Brain Topography)<br clear="none">
<br clear="none">
Event-related or resting data<br clear="none">
<br clear="none">
Signal quality (e.g., gel versus saline, very noisy vs. quite clean)<br clear="none">
<br clear="none">
Reliability of particular metric<br clear="none">
<br clear="none">
Type of connectivity metric (e.g., various kinds of phase measures,<br clear="none">
and the plethora of other connectivity and graph theoretical measures)<br clear="none">
<br clear="none">
MEG vs. EEG ?<br clear="none">
<br clear="none">
Sampling rate? </span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">Type of population ?</span><span style="font-size:10.0pt;">
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> Rate of artifacts per population/sample?</span></p>
</div>
</div>
<div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
</div>
<div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">Individual differences in cleanness or clarity of EEG signal?</span></p>
</div>
</div>
<div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"><br clear="none">
<br clear="none">
<br clear="none">
<br clear="none">
Sample articles that seem to use ICA in relation to connectivity<br clear="none">
metrics are listed below.</span><span style="font-size:10.0pt;">
</span></p>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"> </span></p>
</div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;">Moving forward, it may be beneficial to survey these authors and their findings.</span></p>
</div>
<div>
<p class="yiv9734210705MsoNormal" style="margin-left:36.0pt;"><span style="font-size:10.0pt;"><br clear="none">
de Pasquale, F., Della Penna, S., Sporns, O., Romani, G. L., &<br clear="none">
Corbetta, M. (2015). A dynamic core network and global efficiency in<br clear="none">
the resting human brain. Cerebral Cortex, bhv185.<br clear="none">
<br clear="none">
Lai, M., Demuru, M., Hillebrand, A., & Fraschini, M. (2017). A<br clear="none">
Comparison Between Scalp-And Source-Reconstructed EEG Networks.<br clear="none">
bioRxiv, 121764.<br clear="none">
<br clear="none">
Colclough, G. L., Woolrich, M. W., Tewarie, P. K., Brookes, M. J.,<br clear="none">
Quinn, A. J., & Smith, S. M. (2016). How reliable are MEG<br clear="none">
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