[Eeglablist] Phase calculation in QEEG software

Sat Jul 1 14:08:11 PDT 2017

Hi Arnaud,   I am not sure what you mean by "other methods".   Phase differences are not "methods" they are physical facts based on the physics of waves.  There are a limited number of ways to measure phase differences: 1- simple subtraction of waves at the same frequency and 2- the Cross-Spectrum (FFT or JTFA (Wavelets, Hilbert transform).  Please let me know what other methods there are?  I am certainly open to learning about new methods.
 ICA reconstruction is not a method of measuring phase differences in the EEG.   ICA reconstruction is where one starts with decomposing a large set of EEG time series into independent components so that there is one component for each EEG channel, e.g., 19 channels.   Then one uses regression with a smaller set of components, eg., 18 components to create a new 19 channel time series .  The new 19 channel time series has lost information including the original physics of the wave differences.
The ICA does not "correct" the original time series, it distorts all of the phase differences in the original time series including the artifact free parts.   I know that some in your group think that all EEG is noise and all artifact - however, we know that this is false based on intracellular and extracellular recordings simultaneous with the surface EEG.  Also, the studies I cite show:
That phase differences reflect the underlying physiology and anatomy including DTI white matter 'U' shaped fibers and fasciculi - this is not noise nor artifact and does not need "correction".   
The surface EEG phase differences correlate hightly with MRI T2 relaxation time - - this is not noise nor artifact and does not need "correction".   
The surface EEG phase differences sigificantly change with synaptic rise times such as change in state from wakefulness to sleep - this is not noise nor artifact and does not need "correction". 

The surface EEG phase differences correlate with neuronal packing density - this is not noise nor artifact.  

The surface EEG phase differences exhibit dispersive propagation - this is not noise nor artifact.  

The surface EEG phase differences exhibit independent test re-test reliability > 0.95 - this is not noise nor artifact.    

The surface EEG phase differences correlate highly with various clinical conditions - this is not noise nor artifact.    

There are hundreds of peer reviewed studies cited in the National Library of Medicine on phase differences - this is not noise nor artifact.
The 1st derivative of phase differences are highly reproducable measures of phase locking and phase shift - - this is not noise nor artifact. 
The phase slope index is based on highly reproducable and stable phase differences - this is not noise nor artifact. 
Interesting dialog and thank you for helping me and others to better understand why phase differences are distortion is tolerated or accepted as some type of "correction"?
Cheers,
Robert

On Saturday, July 1, 2017, 3:31:49 PM EDT, Arnaud Delorme <arno at ucsd.edu> wrote:

Dear Robert,
In response to you your message. I did not argue that there was no link between neuronal activity and EEG surface activity. This fact is well established.
However, none of the article you mention show that studying EEG phase on the uncorrected raw data is a better presentation of the brain activity than other approaches (involving ICA or not). In fact, none of the article you mention compare between methods. Actually, I mentioned earlier an article that does show that the method you are using is weaker compared to others - figure 2 of https://www.ncbi.nlm.nih.gov/pubmed/28300640.
Next step in a reasonable scientific discussion would be for you to look at this article and explain why you think their analysis is biased, or cite some other comparison article showing your method to be superior to others (then we could look at it and comment on it).
Best wishes,
Arno

On Jul 1, 2017, at 7:08 PM, Robert Thatcher <rwthatcher2 at yahoo.com> wrote:

Hi Arno,

    I missed your June 24th post and thank you for drilling down on an important issue.

You stated: “We are claiming that your amplifierhowever you tune it and however precise it is cannot accurately determine thephase of the source inside the brain unless you make some corrections andassumptions (ICA being one of them). You are convinced that the uncorrected rawdata is the ground truth which several of us here (and dozens of papers) havedemonstrated not to be the case. “
“As Stefan was mentioning, bring us some scientific proof that phase analysis on the uncorrected raw data (with a reference of your choice) is a better presentation of the activity in the brain than other approaches (involving ICA or not) and we can discuss further.” 

Scientific Proof of the Ground Truth

The scientific proof of the ground truth of the sourcesof the EEG and phase differences is established by the physics of electricity ofsummated synaptic potentials in large masses of synchronized cortical neurons.   Thisfact was established in the 1940s and 1950s by intracellular and extracellularrecordings and the physics of the ground truth of the sources of phasedifferences was established at UCLA in the late 1960s and 1970s.  A review of the science that established thisground truth of the genesis of the EEG and the linkages between the human brainand the scalp recorded EEG is in a book by myself and E. Roy John (see pages51-52 at: http://www.appliedneuroscience.com/FuncNeuro_v1n.pdf)

The linkage of phase differences to differencesin synaptic rise times, synaptic delays, synaptic integration and differencesin conduction velocities was covered especially well by Paul Nunez in his 1981book “Electrical Fields of the Brain”. Paul developed a two-compartmental model of EEG phase differences basedon these underlying physiological factors and provided solid science in supportof the physiological sources of EEG phase differences.  Myself and colleaques were impressed by Paul’sand Walter Freeman’s and Elul and Adey and others demonstrating the groundtruth of EEG phase and we conducted a study to systematically test the groundtruth of EEG phase differences.  Here isa url to this study:  http://www.appliedneuroscience.com/TWO-COMPARTMENTAL_MODEL_EEG_COHERENCE.pdf

We found systematic changes in scalp surface EEGphase differences as a function of distance and direction (packing density) andfrequency (dispersive propagation – see Table III and figure 8).

Hundreds of studies since this time confirmedand replicated these ground truth facts about the physiological basis of EEG phasedifferences.  This includes correlationsbetween phases differences and coherence and MRI relaxation time (see http://www.appliedneuroscience.com/Biophysics%20&%20EEG%20Coherence.pdf).  More recent ground truth studies are thosethat replicate synaptic clustering as measured by Diffusion Tensor Imaging (seehttp://www.appliedneuroscience.com/DTI-ThatcherHumanBrainmapping.pdf)

At NIH as the project manager for the first 128channel EEG system we further explored the ground truth of EEG phasedifferences by measuring shifts in phase differences during voluntary motormovements that were replicable across subjects (see http://www.appliedneuroscience.com/HumanNetworkDynamics.pdf)

More recent ground truth studies of thephysiological basis of EEG phase differences are studies using the Hilberttransform to measure phase differences and the 1st and 2ndderivatives of changes in phase differences to compute the phase lock index,phase shift and phase lock durations, phase slope index, cross-frequencycouplings and phase-amplitude coupling all done without the use of ICAreconstruction.  Here is another nonstudy on the dynamics of phase differences between hubs in the default modenetwork (see http://www.appliedneuroscience.com/Default_Network_LORETA_Phase_Reset-Thatcher_et_al.pdf)

ICA Alternate Universe Reconstruction

ICA was conceived in 1994 (P. Comon) and used todecompose EEG into independent components in the late 1990s & 2000s and is an excellent contribution.  Uniqueand dramatic ICA reconstruction is different than only ICA decomposition, itgoes a step further and falsely assumes that the physiological sources of theEEG are “independent” and proceeds to create an alternate and artificialreality by attempting to create something out of nothing.   Georges and others mean “something out ofnothing” by removing one or more components and with this lower dimensionalspace then create a new time series that replaces the original full dimensionaltime series.   There is a mathematical loss of information bythis process that results in alterations of all the phase differences in the physiologicalsources of the original ground truth and thereby de-coupling the physiology ofthe brain (i.e., Conduction velocities, synaptic delays, synaptic integrationtimes, etc) from the ground truth of the brain. 

The defense by the users of ICA reconstructionis a false equivalence between the ground truth of the EEG and their ICA “alternatetruth” that replaces the ground truth.  Further justification for adulteration of the ground truth is the falseclaim that the EEG is random and full of artifact and therefore there is noharm done.

Lets imagine using ICA reconstruction to alterthe phase differences of photons that originate from stars in the universe thatland on a telescope.   The ICAreconstruction creates an alternate reality where there are distortions inphase differences and no expansion of the universe and no dark energy and nodark matter and no special or general theory of relativity because the groundtruth has been replaced by the new ICA universe.  

Best wishes,

Robert

On Sunday, June 18, 2017, 3:04:21 PM EDT, Robert Thatcher <rwthatcher2 at yahoo.com> wrote:

Hi Arnaud,    Thank you for your comments and I agree with them in general.  Like your group at UCSD we use a lot of different computations including the spectral correlation like Lexicor.  Users have access to a suite of tools that are used at both the surface EEG and at the sources (center voxels of 88 Brodmann areas like what Langer et al first published).  The suite of tools at both the surface and sources include power (including log-log), amp. differences, coherence, phase differences, cross-frequency coherence, frequency band phase-slope index, phase lock duration, phase shift duration, cross-frequency phase slope index and phase-amplitude coupling.  Our replication of the six Hagmann et al hubs involved the use of source correlations.  The exploration of the Default Network used phase shift and phase lock duration.  The genearal replication of the Cantani and deShotten DTI atlas by EEG is the sLORETA and swLORETA source correlations that correspond to cortico-cortical connectivity.
We decided to use the phase slope index to measure the magnitude and direction of information flow rather than the Granger causality of directed coherence because of the criticisms of this method by Nolte and Pascual-Marqui and others.
Here are some urls to studies that use some of these tools:
Phase slope index and Intelligencehttp://www.appliedneuroscience.com/Intelligence%20&%20information%20flow-Thatcher%20et%20al%202016.pdf

LORETA source correlations:http://www.appliedneuroscience.com/ClinEEGNeuro.1_07.Thatcher.pdf

Self-organization and Phase shift and phase lock durationhttp://www.appliedneuroscience.com/PhaseresetDevelopment.pdf

Phase reset and Intelligence:http://www.appliedneuroscience.com/Intelligence-phase_reset_Nature.pdf

Source Correlation Replication of Diffusion Tensor Imaging MRIhttp://www.appliedneuroscience.com/DTI-ThatcherHumanBrainmapping.pdf

Phase reset between Hubs of the Default Network:http://www.appliedneuroscience.com/Default_Network_LORETA_Phase_Reset-Thatcher_et_al.pdf

Best regards,
BobOn Sunday, June 18, 2017, 1:53:11 PM EDT, Arnaud Delorme <arno at ucsd.edu> wrote:

Hi Robert,
It is great to see that things have moved forward.
Compared to regular coherence, in Neuroscience we tend to prefer to compute phase coherence (that removes the contribution of the amplitude of the signal) and amplitude correlation (which is independent of phase). The advantage is that we can separate phase and amplitude correlations effects which are mixed together in standard coherence, and study them independently.
Now it seems that you have a measure that works directly on the phase of of the signal. Your phase difference measure (described in your document http://www.appliedneuroscience.com/Brain%20Connectivity-A%20Tutorial.pdf) is defined as the ratio of smooth quadspectrum and smoothed cospectrum. This looks to me as unnecessary complex as what Lexicor was doing. If you want to test for non-zero phase synchronization, use regular phase coherence and circular statistics to compare phase difference distribution between conditions.
Also, wether working at the channel or source level, I believe it is now critical to use partial directed coherence which factors out indirect coherence path between channels (I feel I need to comment again on the fact that coherence measures should not be computed at the channel level but instead at the source level - the choice of reference bias channel connectivity estimate in a dramatic way as shown in figure 2 of this publication https://www.ncbi.nlm.nih.gov/pubmed/28300640 so they cannot currently be used to determine interaction between brain sites").
Best wishes,
Arno

On Jun 18, 2017, at 9:41 AM, Robert Thatcher <rwthatcher2 at yahoo.com> wrote:

Hi Arno,   For a historical perspective, Lexicor, Inc. went bankrupt around 2000 and prior to that time I was told that they computed a correlation and not coherence.  At least this is what Altan their programmer told me.  He called it the Lexicor "spectral correlation coefficient".  NeuroGuide uses standard Numerical Recipies in 'C' for its computation of the auto and cross-spectra and is well documented including a Signal Generation access to educate studens and professionals.   I would be happy to give you free access to ebook titled "The Handbook of QEEG and EEG Biofeedback".   The download instructions are at: www.anipublishing.com   There are over 250 pages of hands on tutorials and coherence and phase differences are included.  Also, here is a url to document that compares and contrasts the Lexicor spectral correlation to cohernce: http://www.appliedneuroscience.com/Brain%20Connectivity-A%20Tutorial.pdf

Best regards,
Robert

On Sunday, June 18, 2017, 12:34:40 AM EDT, Arnaud Delorme <arno at ucsd.edu> wrote:

Hi Georges,

Regarding the FFT phase calculation, I remember working on the phase measure and trying to reproduce results of the Lexicor QEEG software. I assumed it was a simple pairwise phase coherence measure between channels (like in EEGLAB), but it was not. It was impossible to reproduce until Lexicor shared with us a snippet of code in C they used to compute this phase coherence index. The reason is that the measure was not documented and had some complex phase unwrapping procedure which did not make sense to me. I am wary of these measures, which even if they correlate with behavior or clinical symptoms, have no physiological basis and no clear signal processing basis either (at least I could not find an obvious one). Their meaning is unclear.

I cannot comment on the phase measure calculated in Robert’s popular QEEG Neuroguide software. I just hope it is be better documented than what Lexicor was using. In the end, we are all doing EEG, and should agree on common practices. I do think the QEEG field (often looked down by us neuroscientist) has a lot to contribute. For example, I believe that Robert's extensive database of thousands of clinical cases collected over the past 30 years is a good example to follow. This type of database, assuming the data is collected in controlled conditions, may benefit both the QEEG and the EEG clinical neuroscience fields, and lead to more effective and robust neurofeedback treatment protocols.

Best wishes,

Arno

> On Jun 17, 2017, at 1:42 PM, Georges Otte <georges.otte at telenet.be> wrote:
> 
> Dear Stefan
> 
> First point: 
> 
> "Ecg artefact not being visible in clinical eeg" is  abold statement that awaits proof as it is quite against my (+ 40 years ) experience in neurological and psychiatric eeg. Furthermore You seem to suggest that  EEg is like continuously ( even when not visible) contaminated by all kind of artefact even emg-artefacts.I respect this statement as your personal honest opinion maybe backupped by others but i respectfully dare to doubt that there is a general consensus about this . It is not because EEG is sensitive to artefact due to the amplification factor that alle traces are necessarily or unavoidably noisy or contaminated. If care is taken, as we are thought to do in daily eeg practise and how we train our studies then many artefacts can be prevented. It takes some clinical skills and a technical awareness.  Fi a  Littke astuce i use is to deploy some neurofeedback and show the screen to my patients letting them evoke some artefacts ( chewing, swallowing, head movements, blinks and eye  movements and then sk them to eliminate those artefacts. After some minutes training one gets really nice eeg artefact free recordings. 
> 
> 2 nd point:
> 
> I fully agree  woth you that the ICA technique is subjected to model conditions and constraints. Those are however mostly ignored by clinicians ( who are not researchers nor statisticians) and handing them ICA filters on a kind of push button mode is where the danger resides. 
> The fact that components must be truly independent ( a more stringent condition than uncorrellated), that they must be stationary, and have a non gaussian pdf amongst others are not known to clinicians. Yet the methods are used in clinical theatre where diagnosis will depend on. Lets us agree that it is not because ICA belongs to Blind Source separation thta clinicians should be allowed to use it blindly.
> 
> 3 rd point
> 
> I do not quite understand what yoy mean by mixing DTI ( tractography, neuroimage) with  ADHD ( a clinical syndrome cused by disturbed self regulation) and how these are related to inverse solutions. Do you mean that inverse solutions suchs swLoreta in eeg, MEG and MRI are not acceptable. Roberto Pasquall Marqui will not agree.
> 
> 4 th point
> 
> Unless like yourself, i am deeply concerned with phase and phase relationships between channels and i am sure that many scientists share this deep concern. We have many publications in peer reviewed journals where techniques of phase differences, phase slope index, phase shift and phase lag have been linked to several significant  neuropsychological endophenotypes such as in ADHD,  as autism spectrum disorders, schizo, intelligence et al. 
> If you think these conditions are non trivial as i do, maybe you should give them more concern or  maybe even better, come up with something mo appropriate that wil. Help us clinicians in pinpointing neurophysiological endophenotypes and network architecture patterns. Any suggestions? 
> 
> Sincerely
> 
> Georges
> 
> 
> Verstuurd door Dr. Georges Otte
> Neuro-psychiatrie
> 1/42/652/36/760
> georges.otte at pandora.be
> Consultatie: Bijlokehof 27 9000 Gent (Be).
> GSM: 0032(0)478 205 202
> www.zscoreloretanfb.be
> 
> 
>> Op 17 jun. 2017 om 21:36 heeft Stefan Debener <stefan.debener at uni-oldenburg.de> het volgende geschreven:
>> 
>> Hi there,
>> 
>> I fully agree with Arno. Robert, you may consider the following thoughts:
>> 
>> - We should not forget that many physiological artifacts are more or less continuous in nature. ECG artifacts for instance are usually not visible in the raw EEG, but they clearly exist - for as long as the subject your are recording from is as alive. So, the important implication is that non-brain artifacts near continuoulsy contaminate the recording, even if you don't see them! Just because they are not visible does not mean that they don't exist. This probably also holds for eye-related artifacts (among which many may contribute small voltage, but seem near always present, like microstates with eyes open, or eye ball rotations with eyes closed, etc...) and for muscle-related artifacts (e.g., neck muscles). They are not simply on or off, but more or less active, thus they contribute funny activation patterns to the EEG. It follows that an artifact-rejection approch alone may be misleading, regardless of whether it is implemented by visual inspection or using objective criteria). I personally don't believe that there is any such thing as artifact-free EEG recordings. The artifact contributions are just more or less dominant...
>> 
>> - The ICA model comes with a couple assumptions (like any statistical approach), and if the data do not adhere to the model assumptions then the resulting decomposition may be of very poor quality. In my view, it does not make much sense to praise or condemn a procedure without keeping in mind this fact. Keeping an eye on model assumptions could (and I think, should) guide the preprocessing steps. ICA decomposition quality depends clearly on how the data are preprocessed (e.g.; http://ieeexplore.ieee.org/document/7319296/?reload=true). You may also want to consider the SPR guidelines: http://onlinelibrary.wiley.com/doi/10.1111/psyp.12147/abstract;jsessionid=ADC0B75ACEC7963AA2BCA8A10F317A3F.f02t04.
>> 
>> - I remotely remember Robert or someones else stating in a previous mail that DTI, ADHD and other types of evidence would validate the functional connectivity patterns observed with inverse solutions of 19-channel raw EEG (I got a similar impression when exploring the appliedneuroscience.com website). Well, I find those associations to be not convincing. With or without ICA, the inverse problem should not be neglected, some care is required for the source level interpretation. Playing a little with parameter settings for source analysis is a good reminder that the resulting outcome could be dramatically different for different settings (same holds for channel-level analysis as well - outcome depends on your processing!). In my opinion, source-level analysis can only confirm predictions (i.e., the result matching your priori expectations); I personally don't trust EEG source activations at unexpected locations, they appear, more often than not, spurious to me. In contrast, the use of ICA for eye blink correction seems much, much better validated to me!
>> 
>> - I don't really get the concern of ICA messing up the phase of the continuous EEG signal. Of course, a spatial (or temporal) filter will modify the signal, that's it's purpose, and if the filter attenuates some portions of the signal, the residual signal may have a different phase, amplitude and/or topography. Because for real EEG recordings nobody knows the ground truth, the question should be whether a filter makes the data better or worse (for an excellent discussion on temporal filters making the data better/worse, see: https://www.ncbi.nlm.nih.gov/pubmed/25128257). Assuming that the raw data are a (hopefully linear) mixture of an unknown number of (brain and non-brain) sources, and, more likely than not, that the number of sources contributing are larger than the number of channels (at least for 19-ch this seems pretty obvious to me), NOT processing the data at all does not give a good reference for any comparison. In other words, the raw data do not qualify very well as the gold standard, because they may be messy (i.e., mixed!). More informative would be a comparison (of, say, two different filter approaches) with regard to a particular effect of interest (say, theta and working memory relationship, or any feature one has sufficient evidence to justify a clear prediction).
>> 
>> Best,
>> Stefan
>> 
>> 
>>> Am 17.06.17 um 19:42 schrieb Arnaud Delorme:
>>> Dear Robert,
>>> 
>>> Thank you for your email and for your analysis. A few comments below:
>>> 
>>> 1- I find it hard to believe all of the plots in your compressed document compared artifact free regions of data. For example, you observed 71% change over frontal channels in the delta band (see attached screen capture in the PDF document from PCT DIFF PRE VS POST-DELORME/PRE VS POST-DELORME_4.bmp). This is consistent with selecting portions of data which contains eye-related artifacts as I show in the rest of the attached PDF document.
>>> 
>>> 2- The data you shared has a high density of blinks, there is only a handful segments of data where the subject stops blinking for more than 5 seconds. Even in these clean segments, in the frontal channels, we can see some slow some activity that likely reflect eye movement (or contamination from previous eye movements by the filter you might have used). ICA is able to remove these as well. I am attaching two examples in the PDF document. In between the two blinks below (see document), you can see that the “clean” segment is not that clean. It is still contaminated by eye artifacts at least on the first 3 channels (in order from top to bottom FP1,FP2, F7, F3, Fz, F4, F8). I do not think that anybody would argue that removing this activity amounts to removing brain activity. So it is important to be careful when selecting “clean” data segment.
>>> 
>>> 3. I have removed all portions of data where eye activity was visible preserving only 40 seconds out of 410 in the original file and I am assessing below the data distortion. Spectrum was calculated on non-overlapping 1 minute data segments (I am attaching the script). I am attaching the scalp topography pre vs post in different frequency bands.
>>> 
>>> We can also see that the power at other channels is not affected. Beyond 5 Hz, the power spectrum does not seem to be affected even in frontal channels with very similar scalp topographies. In the attached document, I show the scalp topography pre vs post in different frequency bands. Note the difference in frontal channels at 1Hz and 5Hz. This is due to what is mentioned in 2, that even clean segments are not totally free of artifacts and I believe that these represent true eye artifacts removed by ICA. We can also see that the power at other channels is not affected. Beyond 5 Hz, the power spectrum does not seem to be affected even in frontal channels with very similar scalp topographies. There is no 71% difference in absolute power as in your plot (in my case, it seems to be about 25% at 1Hz over frontal channel and again, this correspond to removed residual eye blinks).
>>> 
>>> 4. P-value need to be corrected for multiple comparisons. You have 19 channels and are looking at 20 frequency bands. That’s 380 t-test/p-value. By chance at the 5% threshold, 19 of these are going to be significant. This is an important limitation of the QEEG field by the way and the reason why QEEG papers rarely make it to reputable journals. And I have heard people say: well if I correct with classical Bonferoni, nothing shows up significant anymore, but there are less aggressive methods to correct for multiple comparison such as False Discovery Rate, the cluster and max method. The QEEG field should use these methods and they could easily be implemented in your software.
>>> 
>>> I am also attaching the script to reproduce my results from the raw EDF data file you shared (it in the PDF).
>>> ICA is not a magical technique but it works pretty well for removing eye artifacts with the least of distortion of the signal. See for example this paper on real and simulated data (not from us http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0003004) concluding "ICA yielded almost perfect correction in all conditions.” There are many other such papers). And I would be the first to agree that there is a lot of subtleties of using ICA for artifact rejection and lot of potential limitations (instabilities in some conditions, issues with numerical precisions (double vs single), lack of exact reproducibility in some other cases because of the random initial conditions, multitude of algorithms). We have tried to address these issues over the years. Even in your dataset, it seems that Infomax ICA converge to at least 2 different solutions (I have run it 10 times on your data at least) yielding slightly different eye artifacts (both seem valid but differ at very low frequencies < 3Hz and very high frequencies above 60 Hz) and I will have to look at that in more details (I suspect this has to do with the stopping rule and the low number of channels in your dataset - I will run some tests and might have to change the default stop threshold for convergence). It remains that ICA is the best we have right now for artifact removal, and I am convinced that a poll of scientist in our field would show that more than 80% of EEG scientist agree with me.
>>> 
>>> I think it is better to rely on published data than argue about a particular dataset, although it is useful as well. At this point we may agree to disagree and EEG users can compare and contrast different approaches. They can download your data and use the script I provide to reproduce my results.
>>> 
>>> Best wishes,
>>> 
>>> Arno
>>> 
>>> PDF document
>>> http://sccn.ucsd.edu/~arno/download/icaphaserebutal.pdf <http://sccn.ucsd.edu/%7Earno/download/icaphaserebutal.pdf>
>>> 
>>> 
>>>> On Jun 15, 2017, at 12:32 PM, Robert Thatcher <rwthatcher2 at yahoo.com <mailto:rwthatcher2 at yahoo.com>> wrote:
>>>> 
>>>> Dear Arnaud,
>>>> 
>>>>  I did statistical comparisons between 1 min & 40 seconds of artifact free EEG in the original EEG recording in with no eye movement artifact and the Win EEG ICA reconstruction and the ICA reconstruction that you did.  I compared two different artifact rejection methods used on the original EEG: 1- manual selections of artifact free data and, 2- the automatic template method of artifact rejection where I hand selected a 10 second sample of artifact free EEG and then used an algorithm that matched the peak-to-peak amplitudes of the 10 second template to the remainder of the record.  There were no statistically significant differences between these two artifact rejection methods.
>>>> 
>>>> Based on the time points of the artifact free data in the original EEG I selected the exact same time points in the Win EEG ICA reconstruction and in your ICA reconstruction. Therefore all three data files contained 1 minute & 40 seconds of the same time points.  I then computed percent differences as well as paired t-tests between the original EEG and the two ICA reconstructions.  Here is a url to download the results:
>>>> 
>>>> http://www.appliedneuroscience.com/STATISTICS OF ARTIFACT FREE EEG VS POST ICA EEG.zip
>>>> 
>>>> As you can see there were very large statistically significant differences between the artifact free EEG in the original recording and the ICA reconstructions.  Your reconstruction was less distorted than the Win EEG reconstruction but both were significantly different than the original artifact free EEG.
>>>> 
>>>> I would be happy to send you the .edfs of the selected time points so that you can verify that the time points were identical and the original EEG did not have any eye movement artifact.
>>>> 
>>>> These large magnitude of the differences between the original and unaltered data vs. the ICA altered data are similar to those that many WinEEG users find when they use the WinEEG ICA reconstruction method.  Therefore these large differences are not surprising and are commonly found especially when using the WinEEG ICA.  For example, Georges or Robert Lawson and others.
>>>> 
>>>> I also included screen captures of some of the waveforms showing visually detectable differences between the original and the ICA reconstruction using the WinEEG ICA.  The ICA that you used produced less visually obvious waveform changes but nonetheless there are some that are visually detectable.  However, the best way to understand the alterations of the artifact free sections is by JTFA and/or FFT and statistics.
>>>> 
>>>> Thank you for your patients in and dedication to exploring this important topic.  It is an important topic because of the obvious discrepancies that will exist in the scientific literature between simple deletion of artifact vs ICA reconstruction going forward.  Also because the entire EEG record is modified the ability to replicate findings is reduced when using ICA reconstruction.  Also, because there is some degree of decoupling between the underlying physiological origins of the EEG and a patient's brain then clinical correlation or effect size will be lower.
>>>> 
>>>> Best regards,
>>>> 
>>>> Robert
>>>> 
>>>> 
>>>> 
>>>> On Thursday, June 15, 2017, 10:11:45 AM EDT, Robert Thatcher <rwthatcher2 at yahoo.com <mailto:rwthatcher2 at yahoo.com>> wrote:
>>>> 
>>>> 
>>>> Hi Arno,
>>>> 
>>>> Thank you for your thoughtful post.
>>>> 
>>>> “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 am referring to the differences in phase between pairs of EEG channels. One can visually see differences in particular segments but it is best to use the Hilbert transform (cross-spectra) to compute instantaneous phase differences at any point in the record that one may want to average the absolute phase differences over some period of the record where there is no artifact and then conduct t-tests to evaluate the large effect sizes.  One can also compare the FFT spectra which is also an average, albeit more noisy.  The alteration of phase differences are present no matter what measure one uses. The least reliable is a visual analysis although there are plenty of visual examples if one carefully reviews the traces.
>>>> 
>>>> "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.”
>>>> 
>>>> Myself and many others do not disagree that elimination of artifact is important what we disagree with is the ICA reconstruction method that adulterates the artifact free segments of the record.  Why not simply delete the eye movement manually or like Neuroguide does with a signal detection algorithm that measures the voltage gradients produced by a blink or eye movement, etc?  In this way all of the original digital data samples are unaltered.
>>>> 
>>>> 
>>>> “The EEG signal is extremely noisy.”
>>>> 
>>>> The vast number of EEG experts would disagree with you that the “EEG is extremely noisy”.  If this were true it would be obvious to every one with a total inability to replicate any EEG study and there would not be over 100,000 peer reviewed studies published in the National Library of Medicine. Simply visually examine the EEG traces showing well behaved and well organized alpha rhythms or theta rhythms or beta rhythms which reflect large synchronous LFPs.
>>>> 
>>>> 
>>>> “The phase of the signal at one electrode site and one given time is not representative of the underlying brain signal.”
>>>> 
>>>> This also cannot be true because the phase difference between electrodes 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 is highly reproducible and clinically useful. If your belief were valid then there would be no clinical correlations to the EEG such as schizophrenia or ADHD or depression or epilepsy or drug effects, etc.
>>>> 
>>>> 
>>>> “, 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).”
>>>> 
>>>> This is an interesting take on my analogy and I agree that the 10-fold size artifact needs to be avoided or eliminated but not by using ICA reconstruction that effects the artifact free parts of the spectrum and thereby 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 distortion in phase”
>>>> 
>>>> I wish that the distortion in phase difference was “minute” but the fact is that it is large and easily demonstrated as it has been by numerous scientists/clinicians over the last few years.  For example, t-tests between the artifact free segments in the original EEG vs. the ICA 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 appreciate your 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 <mailto: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 <mailto: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 <mailto: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 <mailto: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 <mailto: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 <mailto: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 <mailto:rwthatcher2 at yahoo.com>> wrote:
>>>>>>> 
>>>>>>> <Example of Phase Differences at  1min & 46 seconds.pptx>
>>>>>> 
>>>>>> <ICA_phase_example.png>
>>> 
>>> 
>>> 
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