[Eeglablist] FW: Beyond good and evil of ICA

Tue Aug 1 15:37:59 PDT 2017

Makoto,You explained: "I have no experience in using distributed model (I admit that I have been a
'blind believer' on this point so far). My former colleagues, now at Qusp,
were positive to the distributed source models, but I'm not so sure.
Because I can easily imagine far more assumptions are necessary to make the
scheme work. My hunch is that both methods are equally bad :-)"I appreciate your dilligence and deep interest in these topics.  The Distributed inverse solution vs the Discrete solution with one or two dipoles is one that is worth commenting on and I look forward to your reply or comments because the issue of "Discrete" solutions vs "Distributed" solutions is a mature topic that that has hundreds of published validation studies.   Malmivuo and Plonsey, "Bioelectrical Magnetism" , Oxford Univ. Press, 1995. reviews the history starting from the 1800s to today on "distributed" solutions. In the 1980s, Hamalainen and Ilmoniemi (1984) were one of several scientists that applied the distributed solution to the EEG using the Minimum norm.  A problem was localizing deeper sources than those linked to cortical boudaries like the dura or pia.  Numerous distributed solutions were tested at NIH in the early 1990s including stimulating from the tips of implanted electrodes in epilepsy patients where the tips of the electrodes were the "ground truth".  Both "discrete" single dipoles (e.g., Scherg) and "distributed" dipole solutions were evaluated.  We tensallated the cortical surface and used Finite Element Analyses (FEA) and Boundary Element Analyses (BEM) to test and verify different inverse solutions.   The distribited source solutions were quite accurate with more error where the brain deviates from a perfect sphere and then in 1994 Roberto Pascual-Marquie et al, (1994) added LORETA to contrain the distributed inverse solution that had better depth source resolution than the minimum norm. To reduce the spherical model error, sLORETA was developed around 2000 that improved the "distributed" solution to correct for the errors when using a sphere and several other similar solutions were published in the 1990s.  Since the 1990s a few thousand validation and clinical correlation studies of "distributed" inverse solutions have been published.  
Here is a URL to a partial list of studies published as of 2009:http://www.appliedneuroscience.com/LORETA%20publications.pdf

In addition to these studies hundreds of studies since 2009 show that the science of source localization using "distributed" inverse solutions are not "new" or "immature".
The problem is that ICA users falsely believe that the ICA abstract mathematical constructions are "sources" when they are not.
Sincerely,
Robert
On Tuesday, August 1, 2017, 6:00:21 PM EDT, otte georges <georges.otte at pandora.be> wrote:

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Dear friends

For me, as a clinician working on a 19 ch EEG it is now perfectly clear, as Makoto himself has proven and confirmed, that the ICA reconstruction with leaving out components does indeed changes the phase relationship between channels. I find it a bit strange that it took so long to come to this conclusion and there were so many people who at first did not accept this at all  (and maybe they still do not 😊)

However, I felt that the discussion was swinging from phase distortion by ICA reconstruction to the EEG model where a lot of people plaid the card of multisource volume conduction and some even considered the EEG as a continuously artifact contaminated registration where ICA filtering would enhance the purity of the signal as reflection of brain activity.

As volume conduction is translated to the observer by zero phase lag it seems to me that in order to get meaningful data on information flow between brain network nodes we must look at the part of the signal (imaginary part) that can give information of phase delay’s  (fi phase lag index and other measures that contain this information). Therefore I think that phase relationships between channels and signals should be kept unaltered.

Sincerely

Georges 

From: Makoto Miyakoshi [mailto:mmiyakoshi at ucsd.edu] 
Sent: Tuesday, August 1, 2017 11:18 PM
To: Robert Thatcher <rwthatcher2 at yahoo.com>
Cc: otte georges <georges.otte at pandora.be>; Eeglablist <eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] FW: Beyond good and evil of ICA

Dear Robert,

I paste a link below to show mathematical process of how phase CHANGES after rejecting a component obtained by a linear method.

You are calling the difference between 18 Bitcoins and 19 Bitcoins 'distortion'. It's a due change.

https://sccn.ucsd.edu/wiki/How_phase_is_calculated_in_linear_decomposition

If your understanding is different, please show it in math.

Makoto

On Thu, Jul 27, 2017 at 12:30 PM, Robert Thatcher <rwthatcher2 at yahoo.com> wrote:

Dear Makoto,

You stated: "So far I know Montefusco-Siegmunt et al. (2013) is the only paper that makes this invalid claim. If you know other papers, please let me know."

The phase distortion by ICA reconstruction was only discovered in 2014 so there are not a lot of publications on this topic.  However, you are the author of one publication yourself on this Eeglablist.

For example: “If you remove IC and reconstruct channel EEG by back projecting the remaining ICs, of course it changes channel EEG phase!” (Makoto Miyakoshi, Eeglablist ICA and signal phase content, Sept. 16, 2014) 

The proof of the distortion was discovered and validated by comparing the phase differences of the original EEG to the ICA reconstruction time series thereby invalidating the cross-spectrum which is essential for network analyses and also inverse source solutions.  The proof is by observation and mathematics for example by yourself and the following other Eeglablist publishers:

“The EEG reconstruction after removing bad components/sources MAY change the phase value of the signal at any electrode.” (M. Rezazadeh Eeglablist ICA and signal phase content, Sept. 18, 2014).

“The reconstructed data after removing spurious ICA components differs from the original time series, and because of that there are phase differences.” (Arnaud Delorme, Eeglablist ICA misinformation, June 10, 2017).

  “I first noticed the problem with phase distortion more than a decade ago” (Robert Lawson, Eeglablist ICA misinformation, June 14, 2017).

“I think Bob is right that the relative phase will be changed by deleting 1 or 2 artifact components.” (Ramesh Srinivasan, Eeglablist ICA misinformation, June 14, 2017).

“We found phase distortions in the 8-10 Hz alfa band (greatest near the source of artefact) but also on more remote electrodes such as occipital and also in artefact free strokes of EEG.” (Georges Otte, Eeglablist ICA misinformation, June 15, 2017).

Additional proof is by direct comparisons like Arno did showing about 98% of the phase differences are statistically significantly altered at P < 0.0001. Here is a url to some of the statistics and tutorial demonstrations that allow one to verify for themselves:

http://www.appliedneuroscience.com/Tutorial_Adulteration_Phase_Relations_when_using_ICA.pdf

Myself and colleaques will be publishing more statistical comparisons and also show how ICA reconstruction distorts other network measures such as the Phase Slope Index and phase shift and phase lock duration and phase-amplitude coupling and cross-frequency coupling, etc.

Other publications are: 

 Bridwell et al (2016) Spatiospectral Decomposition of Multi-subject EEG: Evaluating Blind Source Separation Algorithms on Real and Realistic Simulated Data. Brain Topogr DOI 10.1007/s10548-016-0479-1 Feb 2016 - see page 13 where they state: “The current group spatiospectral BSS approach discards phase information …” (Pg 13).

R.W. Thatcher (2012) "Handbook of QEEG and EEG Biofeedback" , Anipublishing Co., St. Petersburg, Fl

Otte, G. "ICA Reconstruction"  Presented at the ANT workshop, Beaune, France

I hope that this is helpful.

Best wishes,

Robert

On Thursday, July 27, 2017, 1:58:05 AM EDT, otte georges <georges.otte at pandora.be> wrote:

Dear Bob

I reposted thismessage below  to the EEGLablist and asked Makoko what caused his opinion switch since 2014 ….

Maybe another mail that will get “lost”….   ?  We’ll wait and see….

Sincerely

Georges

From: otte georges [mailto:georges.otte at pandora.be] 
Sent: Thursday, July 27, 2017 7:55 AM
To: 'mmiyakoshi at ucsd.edu' <mmiyakoshi at ucsd.edu>
Subject: RE: Beyond good and evil of ICA

Dear Makoto

Below is the mail I have send to the EEGLab list and that could maybe also be relevant as a reply to Mr. Andrew Smart. 

I can imagine that managing a busy list in extra time is quite a hard task so therefore I can understand that messages get lost in transit. No offense taken.

PS the bitcoin image (a string of chars) is mine. It reflects to the fact that if one has 19 strings or components and omits 4  or 5 the reconstructed ones will not be accepted as true bitcoins. In case I am wrong I will send you my sincere apologies and some char strings (just joking)

Sincerely

PS: (no joke) in a mail You send me in 2014 when we had this discussion again you did state that ICA reconstruction does indeed change phase relations between channels. What causes Your switch of opinion ?

Sincerely

Georges

Mail of july

There is a major evolution in modern neuropsychiatry that aims at linking clinical symptoms to brain network dysfunctions. While this approach was successful in grounding neurological symptoms to structural pathologic alternations in brain networks, in psychiatry the main momentum was not structural but functional network dysfunctions. While fMRI was the pioneer, the much better time resolution of MEG and EEG made them the preferred tools. Their output ( time series) is but a means to further construct a functional image of the networks involved where phase dynamics teach us the directionality of the information flow in the network nodes and allows us by comparison with a database of normal values what functional abnormalities can be detected. For me phase integrity in the data is thus very important to be able to construct valuable graph theory models of those networks be them dysfunctional or compensatory. Much work has been devoted on this topic since many decades by DrThatcher but also by many other authors such as Vinod Menon ( Stanford) linking psychiatric symptoms to specific network dysfunctions. For us, clinicians this introduces a new approach to neuroscientific psychiatry that links psychiatry back to it's neurobiological roots and can hopefully one day send the DSM categorization to the museum of the history of psychiatry.

As phase is IMHO a most important parameter in order to establish the network internode information flow, it should not come as a surprise to hear that some find phase unimportant as contaminated by continuous artefact or hear about ICA’ s a signal reconstruction method that presents the danger of changing the phase dynamics in the original time series especially in low channel (19ch) recordings with perhaps more prominent effect due to overcompleteness. 

If in a 19 ch. EEG a clinician rejects (nulls out the rows of the mixing matrix ) ICAas components for blinks EMG, pletysmo and ecg ( 4 ) and then does a "reconstruction"  ( creating 19 channels  out of 15 ??) what we then get might look nice but is IMHO  not a valid base for a graph theoretical model of the underlying brain network.

I think this is the reason this discussion is important and certainly not a trivial pro or contra ICA pugilism. 

Sincerely

Georges

From: Makoto Miyakoshi [mailto:mmiyakoshi at ucsd.edu] 
Sent: Wednesday, July 26, 2017 11:40 PM
To: Robert Thatcher <rwthatcher2 at yahoo.com>
Cc: EEGLAB List <eeglablist at sccn.ucsd.edu>; Georges Otte <georges.otte at telenet.be>
Subject: Re: Beyond good and evil of ICA

Dear Robert,

I want to know all publications that makes a clear claim that 'ICA distorts phase'. I will include all of them for our clarification paper. So far I know Montefusco-Siegmunt et al. (2013) is the only paper that makes this invalid claim. If you know other papers, please let me know.

Again, you are calling the difference between 18 Bitcoins and 19 Bitcoins 'distortion'. It's a due change. See the pages below.

https://sccn.ucsd.edu/wiki/How_phase_is_calculated_in_linear_decomposition

https://sccn.ucsd.edu/wiki/ICA_phase_distortion

Georges, Ramon told me that all the posts were published on the list. If otherwise, please let us know. Sorry for the trouble.

Makoto

On Wed, Jul 26, 2017 at 12:06 PM, Robert Thatcher <rwthatcher2 at yahoo.com> wrote:

Dear Makoto,

   I think your criticisms are important and note that there are traveling waves in the EEG and also there is nonlinearity in the form of wave dispersion as noted by Nunez, 1981 and demonstrated in the paper that can be downloaded at this url (see Table IV):

http://www.appliedneuroscience.com/TWO-COMPARTMENTAL_MODEL_EEG_COHERENCE.pdf

It seems that your 3rd criticism does not recognize that ICA reconstruction of a new time series violates the "Reciprocity" theorem of Helmoltz and the "Lead Field" necessary for a valid inverse solution.

You mentioned a recent criticism on ICA that you stated is "technically invalid".   I doubt that you are referring to the criticism about ICA reconstruction adulterating phase differences between EEG channels?   The issue of ICA reconstruction and phase alteration is a settled issue based on math (not the separation of mixtures of phase or frequencies but rather the cross-spectrum at the same frequency at different locations) as well as multiple empirical demonstrations and tutorial demonstrations that anyone can verify for themselves.  Also, I am copying from the Eelablist statements by yourself and five others agreeing that ICA reconstruction alters phase differences.  

“If you remove IC and reconstruct channel EEG by back projecting the remaining ICs, of course it changes channel EEG phase!” (Makoto Miyakoshi, Eeglablist ICA and signal phase content, Sept. 16, 2014) 

“The EEG reconstruction after removing bad components/sources MAY change the phase value of the signal at any electrode.” (M. Rezazadeh Eeglablist ICA and signal phase content, Sept. 18, 2014).

“The reconstructed data after removing spurious ICA components differs from the original time series, and because of that there are phase differences.” (Arnaud Delorme, Eeglablist ICA misinformation, June 10, 2017).

  “I first noticed the problem with phase distortion more than a decade ago” (Robert Lawson, Eeglablist ICA misinformation, June 14, 2017).

“I think Bob is right that the relative phase will be changed by deleting 1 or 2 artifact components.” (Ramesh Srinivasan, Eeglablist ICA misinformation, June 14, 2017).

“We found phase distortions in the 8-10 Hz alfa band (greatest near the source of artefact) but also on more remote electrodes such as occipital and also in artefact free strokes of EEG.” (Georges Otte, Eeglablist ICA misinformation, June 15, 2017).

Best regards,

Robert

On Wednesday, July 26, 2017, 2:01:59 PM EDT, Makoto Miyakoshi <mmiyakoshi at ucsd.edu> wrote:

Dear List,

Recently there was a criticism against ICA on the list. Unfortunately it is technically invalid so I remained unsatisfied. Let me share real problems of the ICA model (Onton and Makeig 2006) to re-do it. This is a continued discussion from the one titled 'How phase is calculated in linear decomposition' and now this is my turn to criticize ICA!

As far as I know, there are three known limitations in ICA model.

   - Spatial stationarity. I have seen a nice traveling waves in ECoG grid data during Joaquin Repela's presentation at SCCN. This clearly violates the assumptions of spatial stationarity in ICA.
   - Temporal stationarity. Shawn Hsu at SCCN presented time-series data of ICA model likelihood during drowsy driving task. Also, Jason Palmer's AMICA also demonstrated temporal changes in model likelihood. So one model per data does not fit the truth (unless the task has a strong control over a subject's cognitive and behavioral states).
   - Dipolar source model. Although most of ICA results are fit with dipole models, it seems ICA also returns (probably) non-point sources. When one fits a dipole model to such a non-point source, the location tend to end up with physiologically invalid depth (this is the most annoying thing about ICA today)

I'd like to hear detailed criticism about these points. Note I saw these critical counterevidence in SCCN; we are not a boring ICA cult who have blind belief in it.

Nonetheless, ICA model has a critical merit. I named it Independence-Dipolarity Identity (I-D Identity, or IDId). I-D Identity means that when ICA solves temporally problem, it also solves spatial problem at the same time without using ANY spatial constraint. Dipolarity can be thought of, in short, biophysical origin-ness. Hence I believe that this is evidence that ICA hits some physiological truth of EEG generation.

There could be multiple criticisms against the limitations of ICA model, but at the same time any criticism, at least so far, was NOT strong enough to deny I-D Identity of the ICA model. After all, because of this I-D Identity, I still advocate ICA (but similar dipolarity can be achieved by using very different approach, such as SOBI... so independence is not the only requirement to reach the biophysical validity. It's still a mystery to me.)

All models are wrong, but some are useful... but I want to go beyond this statement to reach the ground truth of EEG!

-- 

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