[Eeglablist] FW: Beyond good and evil of ICA

Tarik S Bel-Bahar tarikbelbahar at gmail.com
Fri Jul 28 11:23:54 PDT 2017

Greetings, it's great to see a more congenial future-focused discussion
​ 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,
​ ​
or point of view, whether based on theory
​, math,​
or experience,

satisfactory until th
​in ​1)
multiple published new high-quality
​ empirical​
​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, ​
​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
​ or that we can add to/clarify​
, and a list of articles (and authors) that
 be consulted regarding current use/opinions of ICA, EEG, phase, and con
​ 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
 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

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.

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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