[Eeglablist] FW: Beyond good and evil of ICA

[Marius Klug]

Hi everyone!

One thing after the other...:


Georges, sure, feel free to use the example, I'm always happy to contribute
:-)

A comparison of high to low density ICA cleaning would definitely be
interesting and my hypothesis is that the ICA decomposition might be
significantly worse (meaning more changes in the posterior channels) in the
19-channels set. If I have some spare time I might do that and post the
results. Will take a while, though, I won't have time until end of
September I think...



Robert, if you read again my very first email on the topic, I have not
changed my mind the slightest bit... I have said from the beginning that
ICA does indeed change the data, which is rather obvious. It's only about
the question where this change is a positive or a negative one. However, as
I said, it might be that 19 channels is not a sufficiently high number for
a decent ICA decomposition for cleaning. It sure is not for working on the
source level, but by now I did indeed assume that the eye components are so
strong that even 19 channels are enough to cleanly take them out. If that's
not the case, it's an important information and everyone needs to decide
for themselves if they want to use ICA and risk losing a slight bit of
brain data or other cleaning methods which might prove to be better in the
low-density segment. I would, however still HIGHLY encourage you to test
whether or not the results with the ICA-based cleaning are significantly
better or worse than without. As long as you have not tested this, you keep
rejecting a method that might prove useful for you.

The as you call it false belief of continuous artifacts has in my opinion
already been adressed enough and I guess you just must have special means
of keeping eye and neck muscles and heart beats from continuous signals in
the EEG, since in ordinary participants eye components seem to correlate
highly with eye tracker signals and I can still see the heart beats of my
subjects in their EEG. As I've stated elsewhere, there are many means of
increasing signal-to-noise ratio, ICA just being one of them (one other
would for example be the averaging of ERPs). Many other published sources
just tell us that those other means work as well and that the signal had
been strong enough for statistically significant results. It does not say
there are no continuous artifacts, that's a logical fallacy.

As for the statistically independent sources in ICA and connected brain
regions: This is an interesting thing to think about, that's true. One
answer would be that ICA has no temporal knowledge while separating the
sources, it just works with point clouds, but the brain connections are
temporal. One other might be that ICA doesn't separate based on correlation
but more complex measures of the data set. I must admit I didn't have the
time yet to fully understand the different ICA algorithms, though. My last
answer to this is the fact that ICA gets nicely dipolar components which
can be projected back to the brain, and this also works in simulations
where the true sources are known. So for me, at least with sufficiently
high density (64 at least, better 128) ICA components that have a neat
separation from other components are brain sources. This has to be taken
with a grain of salt though, since one needs to be experienced in
understanding inhowfar a component might still be mixed sources. For
example residual variance of the component gives at least a hint about this
question. Unless proven otherwise, I will keep my working definition of ICA
components as sources, and especially dipolar sources as brain sources,
since I work on the source level anyway...

Be it as it may, the 18-19 bitcoin example still doesn't hold true, you can
just replace "sources" with "components". You made another strawman
argument which I answered because it's an interesting question, not because
it's an actual counter argument against my case.

Also, I think exactly the central-limit theorem is being used in ICA. A
singular source is always less gaussian than mixed sources, so a de-mixing
matrix which maximizes non-gaussianity of components would maximize the
separation of the components from each other. I think your argument here
goes exactly where the basic ICA argument goes so I don't see your point.
See http://arnauddelorme.com/ica_for_dummies/



Makoto, yes, the dipole model is definitely far from perfect. We also
attempt to create a head model including the eyes and neck muscles, since
the head movements are vitally important for us. Blood vessels is an
interesting point though, we hadn't thought about this, as far as I know at
least. This would definitely improve localization of the muscle artifacts
and simplify the discrimination of eye and muscle, but of course muscles
are complex in their electrical properties so it's going to be an
interesting question if this is working as intended. The distributed source
model does appeal to me in the sense that it's just more realistic than a
single dipole creating the signal at the scalp, but I am not familiar with
the assumptions that are held for this.

Another big issue to me is that we don't get the same components for each
subject. Clustering is a really bad way of combining the components, even
though it's the best we have. In fact, I've realized that the clustering
solution varies to a more-than-slight degree even if you re-cluster with
the exact same parameters. I've thought of a method to do repeated
clustering and compare the results to have a kind of probabilistic model
slightly similar to Nima's Measure Projection, but it has proven to be more
work than I had time. I might come back to it, though! As you said, a long
way to go to make EEG a smoothly working brain imaging technique.. but all
the more interesting and it's fascinating to be part of this!



Georges, I don't think anyone ever doubted that the data changes after one
takes out eye components. It's obvious and I don't recall emails that state
otherwise. The whole discussion was about the fact that the change in the
data would increase the quality and make it closer to the actual brain
data, that was even in Arno's very first post. It's reflected in the
wording, for example you and robert use "distortion" or "adulteration",
while Arno, Makoto and others from this ist use simply "change", because
this implies no negative impact.

I think an important thing to keep in mind is that we can not really make
any meaningful conclusions from sensor level activity as to where this
activity originated from. Sensor-level connectivity will always have this
drawback, plus the mixing in of artifactual sources, which is why Johanna
has said a while ago that she performs only high-density EEG source-level
connectivity analysis, nothing on the sensor-level at all. It obviously
depends on your data set and application, but to me, 19 channel surface EEG
is always to be treated with extra care as to any interpretations.



Tarik, yes, it seems to me that there is tons of method research to be done
and paper to be written. We soon will have a synchronized data set
containing high-density EEG with 28 as EMG on the neck, together with
motion capture of the head, shoulders, hands and feet in position and
orientation and eye tracking while moving around in virtual reality. This
might provide useful for investigating the continuous artifacts, since we
have eye movement, muscle activity, and (if we record this in addition) ECG
all synchronized and we can look for it's connections to the EEG data.
Well, the things to come! It all needs time and resources... Anyways, I
would be glad to contribute to some of the papers if I can, since
investigating the EEG methods is one of my main interests as a researcher.


All the best,
Marius

2017-07-28 20:23 GMT+02:00 Tarik S Bel-Bahar <tarikbelbahar at gmail.com>:

> Greetings, it's great to see a more congenial future-focused discussion
> ​building
>  here.
> ​ We all have knowledge we consider as true, biases that impact our
> perceptions and expectations, and this all sometimes gets in the way of
> (needed) discussion on research topics and assumptions. It seems we often
> need less "fixed ideas and assumptions" and to be more open to "new
> published data-based findings addressing specific topics".
> Overall, kudos for keeping the
> ​human ​
> conversation going
> , it may eventually lead to positive impacts for the EEG field.
>
> From my perspective,
> ​ ​
> argument
> ​s​
> or point of view, whether based on theory
> ​, math,​
> or experience,
> will
> ​remain
>
> ​un​
> satisfactory until th
> ​ey
>  are
> ​in ​1)
> multiple published new high-quality
> ​ empirical​
>  ​
> papers
> ​that 2) examine a ​
> range of topics related to the discussion here about ICA, EEG,
> ​phase metrics, ​
> artifacts, etc... from a
> ​3) ​
> data-and assessment-focused point of view.
> ​It's important to have conversations, but it's new peer-reviewed
> publications that will truly clarify things.​
>
> ​I may be too optimistic, but more collaboration amongst the discussants
> here could generate a half-dozen papers over the next year or two focused
> on these issues. These are opportunities for both young flexible
> researchers, as well as more mature researchers and clinicians.
>
> One thing that has struck me is that the conversation has reflected
> ​1) ​
> a divide between low-density and high-density EEG systems users,
> ​2) the differences in researcher and clinician values, ​
> and
> ​that 3)
> there is enough data
> ​already ​
> out there to provide data-based publications addressing these topics.
> Further, the I think there is a lot of room for theoretical and
> methodological consolidation in the EEG field, but this will take time and
> effort, just as a clear understanding of the nature of true neural sources
> will take time and effort.
>
> Below, a resend of my earlier list of topics that future articles can
> consider
> ​ or that we can add to/clarify​
> , and a list of articles (and authors) that
> ​can
>  be consulted regarding current use/opinions of ICA, EEG, phase, and con
> ​n​
> ectivity
> ​ analyses​
> .
>
>
>
> *Possible dimensions/constraints to consider or compare?*
>
> Channel density and relative total-head coverage
>
> ​Low-density vs. moderate-density vs high-density​
>
> With and without EOG and/or EMG recording ?​
>
> Number/type of artifact
> ​ual detected, number/type of​
> ICs removed
>
> ​IC-based vs. non-IC based artifact removal​
>
> Number/type of artifact visible in channel record
> ​​
>
>
> Clarity/robustness of artifact
> ​ual ICs ​
> (e.g., ICs that are mixed vs. ICs that
> are mixed (containing both artifact and neural info)
>
> Channel-level vs. ICA-level vs. source
> ​-estimated
>  connectivity metrics
>
> Length of epochs/trials
>
> Type of source analysis
>
> Type of reference (e.g., Chella et al., 2016)
>
> Type of ICA/blind source separation (e.g., Bridwell et al., 2016;
> Brain Topography)
>
> Event-related or resting data
>
> Signal quality (e.g., gel versus saline, very noisy vs. quite clean)
>
> Reliability of particular metric
>
> Type of connectivity metric (e.g., various kinds of phase measures,
> and the plethora of other connectivity and graph theoretical measures)
>
> MEG vs. EEG ?
>
> Sampling rate?
>
> Type of population ?
> ​ Rate of artifacts per population/sample?
>
> Individual differences in cleanness or clarity of EEG signal?
>
>
>
>
>
> Sample articles that seem to use ICA in relation to connectivity
> metrics are listed below.
> ​ ​
> Moving forward, it may be beneficial to survey these authors and their
> findings.
>
> de Pasquale, F., Della Penna, S., Sporns, O., Romani, G. L., &
> Corbetta, M. (2015). A dynamic core network and global efficiency in
> the resting human brain. Cerebral Cortex, bhv185.
>
> Lai, M., Demuru, M., Hillebrand, A., & Fraschini, M. (2017). A
> Comparison Between Scalp-And Source-Reconstructed EEG Networks.
> bioRxiv, 121764.
>
> Colclough, G. L., Woolrich, M. W., Tewarie, P. K., Brookes, M. J.,
> Quinn, A. J., & Smith, S. M. (2016). How reliable are MEG
> resting-state connectivity metrics?. NeuroImage, 138, 284-293.
>
> Kuntzelman, K., & Miskovic, V. (2017). Reliability of graph metrics
> derived from resting‐state human EEG. Psychophysiology, 54(1), 51-61.
>
> Siems, M., Pape, A. A., Hipp, J. F., & Siegel, M. (2016). Measuring
> the cortical correlation structure of spontaneous oscillatory activity
> with EEG and MEG. NeuroImage, 129, 345-355.
>
> Toppi, J., Astolfi, L., Poudel, G. R., Innes, C. R., Babiloni, F., &
> Jones, R. D. (2016). Time-varying effective connectivity of the
> cortical neuroelectric activity associated with behavioural
> microsleeps. NeuroImage, 124, 421-432.
>
> Farahibozorg, S. R., Henson, R. N., & Hauk, O. (2017). Adaptive
> Cortical Parcellations for Source Reconstructed EEG/MEG Connectomes.
> bioRxiv, 097774.
>
> Rueda-Delgado, L. M., Solesio-Jofre, E., Mantini, D., Dupont, P.,
> Daffertshofer, A., & Swinnen, S. P. (2016). Coordinative task
> difficulty and behavioural errors are associated with increased
> long-range beta band synchronization, NeuroImage.
>
> Cooper, P. S., Wong, A. S., Fulham, W. R., Thienel, R., Mansfield, E.,
> Michie, P. T., & Karayanidis, F. (2015). Theta frontoparietal
> connectivity associated with proactive and reactive cognitive control
> processes. Neuroimage, 108, 354-363.
>
> Nayak, C. S., Bhowmik, A., Prasad, P. D., Pati, S., Choudhury, K. K.,
> & Majumdar, K. K. (2017). Phase Synchronization Analysis of Natural
> Wake and Sleep States in Healthy Individuals Using a Novel Ensemble
> Phase Synchronization Measure. Journal of Clinical Neurophysiology,
> 34(1), 77-83.
>
> Vecchio, F., Miraglia, F., Piludu, F., Granata, G., Romanello, R.,
> Caulo, M., ... & Rossini, P. M. (2017). “Small World” architecture in
> brain connectivity and hippocampal volume in Alzheimer’s disease: a
> study via graph theory from EEG data. Brain imaging and behavior,
> 11(2), 473-485.
>
> Ranzi, P., Freund, J. A., Thiel, C. M., & Herrmann, C. S. (2016).
> Encephalography Connectivity on Sources in Male Nonsmokers after
> Nicotine Administration during the Resting State. Neuropsychobiology,
> 74(1), 48-59.
>
> Vecchio, F., Miraglia, F., Curcio, G., Della Marca, G., Vollono, C.,
> Mazzucchi, E., ... & Rossini, P. M. (2015). Cortical connectivity in
> fronto-temporal focal epilepsy from EEG analysis: a study via graph
> theory. Clinical Neurophysiology, 126(6), 1108-1116.
>
> Smit, D. J., de Geus, E. J., Boersma, M., Boomsma, D. I., & Stam, C.
> J. (2016). Life-span development of brain network integration assessed
> with phase lag index connectivity and minimum spanning tree graphs.
> Brain connectivity, 6(4), 312-325.
>
> Chung, Jae W., Edward Ofori, Gaurav Misra, Christopher W. Hess, and
> David E. Vaillancourt. "Beta-band activity and connectivity in
> sensorimotor and parietal cortex are important for accurate motor
> performance." NeuroImage 144 (2017): 164-173.
>
> Shou, G., & Ding, L. (2015). Detection of EEG
> spatial–spectral–temporal signatures of errors: A comparative study of
> ICA-based and channel-based methods. Brain topography, 28(1), 47-61.
>
> Kline, J. E., Huang, H. J., Snyder, K. L., & Ferris, D. P. (2016).
> Cortical Spectral Activity and Connectivity during Active and Viewed
> Arm and Leg Movement. Frontiers in neuroscience, 10.
>
> van Driel, J., Gunseli, E., Meeter, M., & Olivers, C. N. (2017). Local
> and interregional alpha EEG dynamics dissociate between memory for
> search and memory for recognition. NeuroImage, 149, 114-128.
>
> Castellanos, N.P., Makarov, V.A., 2006. Recovering EEG brain signals:
> Artifact suppression with wavelet enhanced independent component
> analysis. J. Neurosci. Methods 158, 300–312.
> doi:10.1016/j.jneumeth.2006.05.033
>
> Mehrkanoon, S., Breakspear, M., Britz, J., & Boonstra, T. W. (2014).
> Intrinsic coupling modes in source-reconstructed
> electroencephalography. Brain connectivity, 4(10), 812-825.
>
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