<div dir="ltr"><div class="gmail_default" style="color:rgb(51,51,153)">Hello, this is just a followup with some sample references related to my question. Any thoughts are appreciated.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">
<p class="gmail-MsoListParagraphCxSpFirst" style="text-align:justify"><span style="font-size:10.5pt;font-family:Arial;color:blue">1)<span style="font-variant-numeric:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">
</span></span><span style="font-size:10.5pt;font-family:Arial;color:blue">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.<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="text-align:justify"><span style="font-size:10.5pt;font-family:Arial;color:blue">2)<span style="font-variant-numeric:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">
</span></span><span style="font-size:10.5pt;font-family:Arial;color:blue">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.<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="text-align:justify"><span style="font-size:10.5pt;font-family:Arial;color:blue">3)<span style="font-variant-numeric:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">
</span></span><span style="font-size:10.5pt;font-family:Arial;color:blue">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.<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="text-align:justify"><span style="font-size:10.5pt;font-family:Arial;color:blue">4)<span style="font-variant-numeric:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">
</span></span><span style="font-size:10.5pt;font-family:Arial;color:blue">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.<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="text-align:justify"><span style="font-size:10.5pt;font-family:Arial;color:blue">5)<span style="font-variant-numeric:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">
</span></span><span style="font-size:10.5pt;font-family:Arial;color:blue">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.<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="text-align:justify"><span style="font-size:10.5pt;font-family:Arial;color:blue">6)<span style="font-variant-numeric:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">
</span></span><span style="font-size:10.5pt;font-family:Arial;color:blue">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.<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpMiddle" style="text-align:justify"><span style="font-size:10.5pt;font-family:Arial;color:blue">7)<span style="font-variant-numeric:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">
</span></span><span style="font-size:10.5pt;font-family:Arial;color:blue">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.<span></span></span></p>
<p class="gmail-MsoListParagraphCxSpLast" style="text-align:justify"><span style="font-size:10.5pt;font-family:Arial;color:blue">8)<span style="font-variant-numeric:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">
</span></span><span style="font-size:10.5pt;font-family:Arial;color:blue">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.<span></span></span></p><p class="gmail-MsoListParagraphCxSpLast" style="text-align:justify"><span style="font-size:10.5pt;font-family:Arial;color:blue"><br></span></p><p class="gmail-MsoListParagraphCxSpLast" style="text-align:justify"><span style="font-size:10.5pt;font-family:Arial;color:blue"><br></span></p><p class="gmail-MsoListParagraphCxSpLast" style="text-align:justify"><span style="font-size:10.5pt;font-family:Arial;color:blue"><br></span></p><p class="gmail-MsoListParagraphCxSpLast" style="text-align:justify"><span style="font-size:10.5pt;font-family:Arial;color:blue"><br></span></p>
</div><div class="gmail_extra"><br><div class="gmail_quote">On Sun, Nov 12, 2017 at 5:02 PM, Tarik S Bel-Bahar <span dir="ltr"><<a href="mailto:tarikbelbahar@gmail.com" target="_blank">tarikbelbahar@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div style="color:#333399">Hello eeglabers,</div><div style="color:#333399"><br></div><div style="color:#333399">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 ?</div><div style="color:#333399"><br></div><div style="color:#333399">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).</div><div style="color:#333399"><br></div><div style="color:#333399">If you know of any papers that actually retain a small number of neural ICs for their analyses, that would be great too.</div><div style="color:#333399"><br></div><div style="color:#333399">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.</div><div style="color:#333399"><br></div><div style="color:#333399">Thanks very much for any suggestions!</div></div>
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