[Eeglablist] ICA Misinformation

[Arnaud Delorme]

>    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 <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 <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 <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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