[Eeglablist] Beyond good and evil of ICA

Andrew Smart andrew.johnsmart at gmail.com
Wed Jul 26 15:42:49 PDT 2017

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

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

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,

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

> 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_
>> 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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Andrew Smart

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