[Eeglablist] Beyond good and evil of ICA

Thu Jul 27 16:17:02 PDT 2017

Dear Makoto,

thank you for this thread, this is vital to keep investigations in actual
problems of ICA and spatial filters active and on point!

The spatial stationarity is especially a problem as soon as you investigate
any kind of muscle, as you've shown once yourself (I believe?), which is
important for MoBI research. But yes, also cortical traveling waves might
pose a more than trivial problem... The way spatial filters are, it's not
really solvable, is it?

It also goes together with the problem of temporal instationarity. It would
effectively mean that we need a new filter for each frame of the traveling
wave (for max resolution), which would disable any meaningful
interpretation the way its done right now, right? A hard nut to crack for
sure... As for different models for different tasks: I think this is also a
result in itself. It shows nicely that the brain has different working
modes, or states, if it solves different tasks. We thought of taking
multiple models of the AMICA into account, but stick with one for now until
we have everything working smoothly. Interesting thing!

And for the last point: I heard a talk a while ago where source
localization wasn't done based on dipoles but on specific areas most likely
to be the origin of a filter. It was also different for different
algorithms, meaning the areas were not Brodmann, but different per
algorithm used and the boundaries were based on their inaccuracies etc. Do
you know more of this and if so what is your thought here? I'm afraid I
cannot do more research at the moment since I don't have access to a PC for
a while (I'm writing on my phone)...

Robert,

those quotes show that we all agree that the phase is different if data is
taken out. This is no surprise at all, since, obviously, data was taken
out. The claim is that the data that was taken out was artifact. As I've
written, it might be that this claim does not hold true perfectly with 19
channels BUT this is untested. It may be that the changed phase is the
better, i.e. more "brainy" phase, and results for tests are more precise.
It may be that they are not, because a sufficiently large amount of brain
data has slipped into the eye component (not impossible with 19 channels).
The latter would be a problem, and investigating this would be important
(writing the same things on the eeglablist all the time not so much) and it
would answer Andy's second question.

The 19-18 bitcoins example does not hold true either. One does create the
same amount of channels from fewer sources. Not from fewer channels. The
latter would be interpolation and is also regularly used, by the way. The
former takes specific sources out and simulates a recording without these
sources. Both final data matrices have a reduced rank. It is more so that
the EEG contains a few gold nuggets (brain sources) and many stones
(artifact sources) crushed and mixed together and then placed in a few
bowls (channels), and ICA tries to separate those and created the most
accurate representation of gold nuggets and stones it can. The most obvious
stones are then taken out and the remaining gold and a smaller amount of
stone powder is then placed back into the bowls again. They are a little
less filled then (rank reduction) and the quality of the powder in the
bowls is different (phase has changed) because a stone is missing. If there
are only 19 channels (and also with high-density, but less gravely so) the
ICA cannot separate gold from stone perfectly so there is a risk of
throwing away some gold. There are other algorithms (spatio-spectral
decomposition, SSD, for example) that separate based on different criteria,
but in the end all are the same train of thought: Unmixing the mix of gold
and stone. If one does not think there is stone in the bowls one of course
thinks all changes lower the value. This is an assumption at least as big
as the assumptions underlying ICA...

I hope this is a nice example and helps all readers understand the topic a
little better! Andy, has this helped you with your first question? I cannot
contribute to the second, but I do think it is important to check!

Best,
Marius

Am 27.07.2017 20:53 schrieb "otte georges" <georges.otte at pandora.be>:

Dear Andrew

As a clinical neuropsychiatrist I completely share your concerns as they
boil down to the question: “do we need to conserve the information
contained in the raw EEG data recorded at the scalp  or will cleaned up
(ICA reconstruction) data will provide a better view on the brain
functioning?”

I have tried to address this in a mail at the beginning of this month but
it got probably lost. See below.

Basically, for me (but that is a personal view) the time series of EEG
recorded at the scalp contain information about network node communication
in health and disease. This information is contained in the phase
differences between the sources (brain sources). As we all know EEG can be
contaminated by activity originating from non-brain sources or even
technical (non biological) causes.  Eliminating parts of the EEG that are
contaminated by those elements is a daily job (as is taking care on
recording situations with patients so that artefacts are minimal).

There is a large class of methods available for elimination of artefacts.
The classical ones keep the phase content (phase lag differences) between
the channels intact and some may not do that.

Personally, I would avoid methods that alter this information and prefer
methods that do not.

Sincerely

Georges

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:* Andrew Smart [mailto:andrew.johnsmart at gmail.com]
*Sent:* Thursday, July 27, 2017 12:43 AM
*To:* mmiyakoshi at ucsd.edu
*Cc**:* Robert Thatcher <rwthatcher2 at yahoo.com>; EEGLAB List <
eeglablist at sccn.ucsd.edu>; Georges Otte <georges.otte at telenet.be>
*Subject:* Re: [Eeglablist] Beyond good and evil of ICA

Hi all,

I am somewhat of an outsider in this discussion so forgive my limited
understanding as I haven't worked on EEG and ICA for a few years, but I am
fascinated by this debate and many thanks for the clear and reasoned
arguments from all sides. I have since worked in clinical science and FDA
regulated areas with sensors and sensor data - and so have some familiarity
with the validation required for example to use sensor data as a clinical
endpoint.

I have two questions regarding this discussion that I am not understanding
entirely:

1) The idea of phase distortion as opposed to "true" brain phase. I would
like to understand better what the arguments are for saying that the phase
of the raw channel data is the ground truth (for lack of a better phrase)
and that ICA distorts this "true" phase (this is my understanding of one
side of the debate). It seems all agree that ICA changes the relative phase
of the channel data - but the debate is about whether this is in fact a
distortion? I.e., is the raw channel data somehow a better representation
of the "true" electrical activity of the brain? It seems like the crux of
the debate is whether the raw EEG is "truer" than the ICA cleaned data -
from my perspective it seems like the ICA reconstructed time series is
closer to whatever "true" underlying brain signals are contributing to the
scalp recording.

Another way to ask maybe: I don't understand what we're supposed to be
comparing ICA phase to and why it's a distortion? A distortion of what? One
way of looking at it is that ICA is actually correcting the phase by
removing artifacts, not distorting it - is that fair?

2) Has anyone filed a 510k to FDA for example using ICA on EEG data for a
medical purpose? I.e., where the intended use of the ICA results is to
diagnose or treat neurological disease? My question is really - what is the
clinical relevance of this discussion?

Many thanks,

Andy

On Wed, Jul 26, 2017 at 2:40 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
wrote:

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.

   1. 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.
   2. 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).
   3. 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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