[Eeglablist] ICA Misinformation

[Robert Thatcher]

Hi Arno,

Thank you for your thoughtfulpost.  

“As far as phase distortionafter removing ICA components (in my decomposition), I am not sure what you arereferring to. Is it the minute shift when the red and black curve do notexactly superpose.”
I am referring to the differencesin phase between pairs of EEG channels.  One can visually see differences in particular segments but it is best touse the Hilbert transform (cross-spectra) to compute instantaneous phasedifferences at any point in the record that one may want toaverage the absolute phase differences over some period of the record wherethere is no artifact and then conduct t-tests to evaluate the large effect sizes.   One can also compare the FFT spectra which isalso an average, albeit more noisy.   The alteration of phase differences arepresent no matter what measure one uses.  The least reliable is a visual analysis although there are plenty ofvisual examples if one carefully reviews the traces.

"I would argue that the data after removing ICA artifacts reflectmore brain activity than before, and that the minute shift is due to removal ofsmall eye movement activity. I agree that this would have to be demonstrated,and that you cannot take my word for it.”

Myself and many others do not disagree that elimination ofartifact is important what we disagree with is the ICA reconstruction methodthat adulterates the artifact free segments of the record.  Why not simply delete the eye movementmanually or like Neuroguide does with a signal detection algorithm thatmeasures the voltage gradients produced by a blink or eye movement, etc?  In this way all of the original digital datasamples are unaltered.

 

“The EEG signal is extremely noisy.”

The vast number of EEG experts would disagree with you that the “EEGis extremely noisy”.   If this were trueit would be obvious to every one with a total inability to replicate any EEGstudy and there would not be over 100,000 peer reviewed studies published inthe National Library of Medicine.  Simply visually examine the EEG traces showing well behaved and wellorganized alpha rhythms or theta rhythms or beta rhythms which reflect large synchronous LFPs.

 

“The phase of the signal at one electrode site and one giventime is not representative of the underlying brain signal.”

This also cannot be true because the phase difference betweenelectrodes and/or sources are produced by the physiological foundations of the brain and networks and are due to differences in synaptic rise times,synaptic integration times, differences in conduction velocity, etc.  This is the underlying brain signal and it ishighly reproducible and clinically useful. If your belief were valid then there would be no clinical correlationsto the EEG such as schizophrenia or ADHD or depression or epilepsy or drugeffects, etc.

 

“, if you have a picture of a star, would yourather remove a visual artifact that is 10-fold the size of your originalsignal or continue to look at your original signal (not being able to see muchbecause of the large artifact masking most of it).”

This is an interesting take on my analogy and I agreethat the 10-fold size artifact needs to be avoided or eliminated but not by usingICA reconstruction that effects the artifact free parts of the spectrum andthereby distorts the measurement not only of the one star that you are looking at but also all other stars and planets in the universe.

 

“Even if ICA was introducing minute distortionin phase”

I wish that the distortion in phase difference was “minute” butthe fact is that it is large and easily demonstrated as it has been by numerous scientists/clinicians over the last few years.  Forexample, t-tests between the artifact free segments in the original EEG vs. theICA reconstructed new time series are mostly significant at P < 0.00001.   I will do some additional statistical comparisons so that you can better understand the large effect sizes of ICA phase difference distortion.  

This is an important dialog and I appreciateyour dedication and willingness to consider these issues.  

 

Best wishes,

 

Robert


On Wednesday, June 14, 2017, 11:49:54 PM EDT, Arnaud Delorme <arno at ucsd.edu> wrote:

Dear Robert,
There is no need to remove more components - except maybe for temporal muscles components (I would have to look again at your data to see if I can identify any). The procedure is to identify a handful of artifact components, remove them and then your data is cleaned of these artifacts. I personally rarely identify more than 4 artifact component in a given subject (some other researchers have a more aggressive approach and remove more). I like to remove components I am sure of.
ICA is a linear decomposition that isolate sources which are maximally independent. Blinks are mostly independent of brain activity (on first approximation) so ICA is able to isolate them.
"You already showed that two ICA component removals results in more phase distortion than the removal of one ICA component.” In all of our exchanges I have always removed 2 components. I have never removed one ICA component.
As far as phase distortion after removing ICA components (in my decomposition), I am not sure what you are referring to. Is it the minute shift when the red and black curve do not exactly superpose. I have two comments on that.
I would argue that the data after removing ICA artifacts reflect more brain activity than before, and that the minute shift is due to removal of small eye movement activity. I agree that this would have to be demonstrated, and that you cannot take my word for it.
"Even a small amount of adulteration or distortion of EEG phase differences is not good and must be avoided at all costs. This is analogous to the use of telescopes that measure phase differences in the spectrum from stars moving in the universe."
The data we are looking at on the scalp is a summation of millions of neuron activity and the phase we are observing a cumulative average of this signal (pondered by the geometry of the brain, difference in conductivity of different tissues etc...). The EEG signal is extremely noisy. The phase of the signal at one electrode site and one given time is not representative of the underlying brain signal. Even if ICA was introducing minute distortion of the "true phase" at given channels, properly removing artifacts (which are 10-fold the amplitude of brain EEG signal) like ICA does is more important than preserving the exact phase at a given time. In your analogy of looking at stars, if you have a picture of a star, would you rather remove a visual artifact that is 10-fold the size of your original signal or continue to look at your original signal (not being able to see much because of the large artifact masking most of it). Even if ICA was introducing minute distortion in phase (which I do not believe it does because it deals with instantaneous mixtures) , it is worth it given the advantage it provides.
The exact phase at one electrode site is not informative in itself. Differences in phase between 2 electrode sites is not informative either because there may be dozens of possibility for activity within the brain to generate such phase difference. One must move to the source level, and this is what ICA is doing (although see also my previous message).
Best wishes,
Arno


On Jun 14, 2017, at 6:50 PM, Robert Thatcher <rwthatcher2 at yahoo.com> wrote:

Arnaud,   It is interesting to see of the amount of distortion of phase differences of the original artifact free segments of the EEG record by ICA is a linear or nonlinear function of the number of ICA components that are removed to do the reconstruction of a different time series.  You already showed that two ICA component removals results in more phase distortion than the removal of one ICA component.  If you were to remove three and then reconstruct and then four and then five, etc and reconstruct and then attach the  .edf files and share them with the forum then we can plot the magnitude of phase distortion of the artifact free sections of the original record due to the ICA reconstructions.   Based on embedding theory one would expect a linear relationship but there may be a nonlinear relationship with an asymptote at about two removals given there are only 19 channels.
Please try this experiement with one or more EEG dataset, the one that was produced by ICA reconstruction in Australial is a good starting point but it will be good to do this experiment with two or three other EEG recordings.
Thank you for honest interest in exploring the extent of phase difference distortion by ICA so that we can better understand it.
Best regards,
Robert 
On Wednesday, June 14, 2017, 9:00:58 PM EDT, Robert Thatcher <rwthatcher2 at yahoo.com> wrote:

Arnaud,    It does not make any difference which components that the scientist/clinicians removed because your own analyses confirmed phase difference distortion by ICA when your removed your own components.  Please try different ICA component removal and attach the edf files to see if you can create a reconstruction of the time series that DOES NOT distort or corrupt the phase differences between channels in the original EEG recording.  Up to this point in time you have resoundeding proven that ICA reconstruction oes distort phase differences no matter what reconstruction is used.
It is important to recognize and to pubically accept that phase or time differences between channels in the EEG is due to physiological processes like differences in synaptic rise times, differences in synaptic summation times and differences in conduction velocities, etc.   Even a small amount of adulteration or distortion of EEG phase differences is not good and must be avoided at all costs.
This is analogous to the use of telescopes that measure phase differences in the spectrum from stars moving in the universe.  If ICA were used to distort the phase differences in the spectrum measured by telescopes because one believes that all telescopes have artifact then we would not know huge amounts about the nature and future of the universe.  The same is true for the human EEG.
Bob

On Wednesday, June 14, 2017, 7:23:43 PM EDT, Arnaud Delorme <arno at ucsd.edu> wrote:


   Thank you for attaching your ICA reconstructed edf file.  It involved removal of two ICA components and the magnitude of changes in phase differences between channels is greater than the one provided by the scientists/clinicians in Australia that deleted only one ICA component.  This is consistent with Taken's theorum and also differential geometry theorums dealing with manifold mapings and Lie groups etc.  I know for certain that they used ICA and not PCA.

Yes, I meant that the data is usually preprocessed by PCA before doing ICA in commercial softwares, which could be the problem (although I do not think it was in that case). We would need to see which components were removed.Best wishes,
Arno


ICA is excellent in feature detection and the brain operates by highly efficient sub-clusters of neurons extracting features, e.g., face recognition by combining features like eye brows, head shape, ears, chin, etc 
Here is a url to a recent study showing that only 206 neurons are necessary to encode face recognition in monkeys:
http://dx.doi.org/10.1016/j.cell.2017.05.011

The individual face components are like ICA face components for face recognition.  However, the anterior temporal lobes are just one node among many nodes in a network so that the monkey can make the correct adaptive decisions in very short periods of time by network coherence and phase locking and phase shifting with other nodes in networks.
The problem with ICA is in its use in artifact rejection and then reconstruction of a new time series that results in a new time series that is disconnected from brain network connectivity dynamics of phase shift and phase lock and coherence and cross-frequency coupling and phase amplitude coupling, etc.
Thank you again and lets continue to seek answers to how best to use ICA for network dynamics without adulterating the original time and phase relations between parts of the brain.
Robert

On Wednesday, June 14, 2017, 6:21:32 PM EDT, Arnaud Delorme <arno at ucsd.edu> wrote:

Hi Robert,

The Australian data was analyzed by two scientists/clinicians in the audience of a workshop that I was doing in 2014 and they are the ones that did the ICA component selection using commercial WinEEG software and not me.

Most commercial EEG software preprocess the data using PCA to reduce the dimensionality of the data. The idea behind this is that users should not have to go through as many components as channels. It is easier to have them select components within 5 or 10 exemplars. However this PCA dimension reduction can bias the reconstruction (we have data to back this up but it is not published yet).
However, I do not think PCA dimension reduction before running ICA was responsible for what you observed (because your data is very clean and even after PCA and the 2 artifact components have huge contribution to the data variance, you would get very similar components). I think the WinEEG users simply did not select the correct artifact components, or maybe WinEEG failed to implement ICA correctly.

You are welcome to download NeuroGuide and install and launch and then paste the key A into an email to me.  I have posted a tutorial on our webpage but I can create a better tutorial to reduce the learning curve.  Similarly when I am able to concentrate on EEGlab then you can tutor me to reduce my learning curve.  Here is a url to the download webpage:http://www.appliedneuroscience.com/Download_NeuroGuide.htm

At the end of the day together lets find ways to use the full power of ICA to explore network dynamics which is my favorite topic and also one that future science depends on.

Yes, I agree on that view. Exploring network dynamics with ICA is not an easy topic. The trend these days is not to use ICA for connectivity analysis but instead define regions of interest and compute pairwise connectivity between all brain regions as in this recent paper https://www.ncbi.nlm.nih.gov/pubmed/28300640. What can be done is to use ICA components to define these regions and compute activity in these regions. It is an open area of research.
Best wishes,
Arno


Best regards,
Robert

On Wednesday, June 14, 2017, 4:40:43 PM EDT, Arnaud Delorme <arno at ucsd.edu> wrote:

Dear Robert,
There does seem to be a phase difference in your powerpoint. However, it is important to know which ICA component you removed to understand why this is the case. Are you sure these were artifactual components? Removing brain components may alter the phase of the signal recorded on the scalp (it would be as if you were removing from the scalp signal the contribution of a brain area). Without that information, it is not possible to figure out the origin of the phase difference. 
This seems to be the same data you shared yesterday. I have looked at it. Black is before ICA and red after removing the 2 eye components. You can see that there is no phase shift at 102.43 second after I remove the two artifactual ICA components. I have provided the code in my email yesterday if you want to reproduce this result in EEGLAB.
Best wishes,
Arno
<ICA_phase_example.png>

On Jun 14, 2017, at 11:14 AM, Robert Thatcher <rwthatcher2 at yahoo.com> wrote:
<Example of Phase Differences at   1min & 46 seconds.pptx>

<ICA_phase_example.png>




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