[Eeglablist] references for usually low number of retained ICs?

[Tarik S Bel-Bahar]

Hello, this is just a followup with some sample references related to my
question. Any thoughts are appreciated.


1)     The following chapter (seems to) suggest the position that there is
a small universe of possible neural/cognitive ICs in EEG datasets: Makeig
S, Onton J. ERP features and EEG dynamics: An ICA perspective. In: Luck S,
Kappenman E, editors. Oxford Handbook of Event-Related Potential
Components. Oxford University Press; 2009.

2)     The following article (seems to suggest) that there is a common
small set of ICs that regularly show up with ICA decompositions of EEG
data: Delorme, A., Palmer, J., Onton, J., Oostenveld, R., & Makeig, S.
(2012). Independent EEG sources are dipolar. PloS one, 7(2), e30135.

3)     The following article is an example of a study where a small number
of ICs are kept for analyses: Steele, V. R., Anderson, N. E., Claus, E. D.,
Bernat, E. M., Rao, V., Assaf, M., ... & Kiehl, K. A. (2016). Neuroimaging
measures of error-processing: Extracting reliable signals from
event-related potentials and functional magnetic resonance
imaging. Neuroimage, 132, 247-260.

4)     The following article is an example of a study where a small number
of ICs are kept for analyses, in this case using MEG: Urbain, C. M., Pang,
E. W., & Taylor, M. J. (2015). Atypical spatiotemporal signatures of
working memory brain processes in autism. Translational psychiatry, 5(8),
e617.

5)     The following article is an example of a study where a small number
of ICs are kept for analyses of mediofrontal activity: “The mean number of
resulting maximally independent and localizable EEG components used in
subsequent analysis was 15 per subject (range: 7 to 26)”. Onton, J.,
Delorme, A., & Makeig, S. (2005). Frontal midline EEG dynamics during
working memory. Neuroimage, 27(2), 341-356.

6)     The following article is an example of a study where a small number
of ICs are kept for analyses: “6.8±5.5 of the top 30 components were
removed from each EEG recording”. Wu, J., Srinivasan, R., Kaur, A., &
Cramer, S. C. (2014). Resting-state cortical connectivity predicts motor
skill acquisition. NeuroImage, 91, 84-90.

7)     Using a large number of datasets and examining the reliability of
ICA decompositions, the following paper found only about 15 reliable
clusters of ICs across participants (including about 10 neural IC clusters
and about 5 artifactual IC clusters), and a median of 18 to 20 ICs per
dataset that had <5% dipole-fitting residual variance: Artoni, F.,
Menicucci, D., Delorme, A., Makeig, S., & Micera, S. (2014). RELICA: a
method for estimating the reliability of independent
components. NeuroImage, 103, 391-400.

8)     Examining a broad range of blind-source separation ICA algorithms,
~5 to 15 reliable ICs were found by each algorithm. Bridwell, D. A.,
Rachakonda, S., Silva, R. F., Pearlson, G. D., & Calhoun, V. D. (2016).
Spatiospectral Decomposition of Multi-subject EEG: Evaluating Blind Source
Separation Algorithms on Real and Realistic Simulated Data. Brain
topography, 1-15.






On Sun, Nov 12, 2017 at 5:02 PM, Tarik S Bel-Bahar <tarikbelbahar at gmail.com>
wrote:

> Hello eeglabers,
>
> Does anyone know of specific references (articles/chapters) that specify
> that which we know from practice, namely that usually there are only ~5 to
> 20 interpretable neural ICs in HD-EEG decompositions ?
>
> My understanding is that several review chapters and articles from the
> SCCN group show this fact (...that there is a limited number of
> real/valid/interpretable neural ICs in most ICA decompositions of EEG data).
>
> If you know of any papers that actually retain a small number of neural
> ICs for their analyses, that would be great too.
>
> I understand there are some researchers that only remove artifactual
> components and keep in any others (so as not to drop neural signals
> unnecessarily). However, it is also the case that some researchers keep
> only a small select of neural/valid ICs, whether or not they "stay in ICA
> space" or "reconstruct the EEG data" for their analyses.
>
> Thanks very much for any suggestions!
>
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