<html><head></head><body><div style="font-family:Helvetica Neue, Helvetica, Arial, sans-serif;font-size:13px;"><div><div>
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<p class="ydp64b2c7a3MsoNormal"><span style="font-size: 14pt; line-height: 107%; font-family: "Times New Roman", serif; color: rgb(38, 40, 42); background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;">Rob,</span></p>
<p class="ydp64b2c7a3MsoNormal" style="margin: 6pt 0in; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;"><span style="font-size: 14pt; font-family: "Times New Roman", serif; color: rgb(38, 40, 42); background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;"> ICs are not sources they are mathematical
constructs used to decompose a mixture of signals into independent factor or
components. It is important to recognize
that ICA </span><span style="font-size:14.0pt;font-family:"Times New Roman",serif;mso-fareast-font-family:"Times New Roman";color:#222222">is based on two invalid
physiological assumptions:</span></p>
<p class="ydp64b2c7a3MsoNormal" style="margin-bottom: 1.2pt; margin-left: 38.4pt; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;"><!--[if !supportLists]--><span style="font-size:14.0pt;font-family:"Times New Roman",serif;mso-fareast-font-family:"Times New Roman";color:#222222">1.<span style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><!--[endif]--><span style="font-size:14.0pt;font-family:"Times New Roman",serif;mso-fareast-font-family:"Times New Roman";color:#222222">The source signals are independent of each
other.</span></p>
<p class="ydp64b2c7a3MsoNormal" style="margin-bottom: 1.2pt; margin-left: 38.4pt; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;"><!--[if !supportLists]--><span style="font-size:14.0pt;font-family:"Times New Roman",serif;mso-fareast-font-family:"Times New Roman";color:#222222">2.<span style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: "Times New Roman";"> </span></span><!--[endif]--><span style="font-size:14.0pt;font-family:"Times New Roman",serif;mso-fareast-font-family:"Times New Roman";color:#222222">The values in each source signal have
non-Gaussian distributions.</span></p>
<p class="ydp64b2c7a3MsoNormal" style="margin-bottom: 1.2pt; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;"><span style="font-size:14.0pt;font-family:"Times New Roman",serif;mso-fareast-font-family:"Times New Roman";color:#222222">ICA
decomposition is a useful academic method to evaluate components or factors
that are created based on these assumptions but ICs are not themselves
electrical sources of the EEG. ICA-R or
reconstruction of a new time series after removing ICs distorts the original electrical
field produced by the brain and therefore source localization based on the reconstructed
time series is a violation the Lead Field and Poisson’s equation and Green’s
function which are at the foundation of the inverse solution.</span></p>
<p class="ydp64b2c7a3MsoNormal" style="margin-bottom: 1.2pt; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;"><span style="font-size:14.0pt;font-family:"Times New Roman",serif;mso-fareast-font-family:"Times New Roman";color:#222222"> </span></p>
<p class="ydp64b2c7a3MsoNormal" style="margin-bottom: 1.2pt; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;"><span style="font-size:14.0pt;font-family:"Times New Roman",serif;mso-fareast-font-family:"Times New Roman";color:#222222">Bob</span></p>
<!--EndFragment--><br></div></div><div><br></div><div><br></div><div id="yahoo_quoted_3260407369" class="yahoo_quoted"><div style="font-family:'Helvetica Neue', Helvetica, Arial, sans-serif;font-size:13px;color:#26282a;"><div>On Monday, August 14, 2017, 1:08:02 PM EDT, Rob Coben <drcoben@gmail.com> wrote:</div><div><br></div><div><br></div><div><div id="yiv9537296241"><html xmlns="http://www.w3.org/TR/REC-html40"><head><style><!--
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--></style></head><div><div class="yiv9537296241WordSection1"><p class="yiv9537296241MsoNormal">We have conducted a study assessing resting eeg in adults that were traumatized as children and wish to compare them to control subjects without such problems in their history. Our primary data to analyze is based on 64 ch eeg sampled at 2000 c/s over two separate recordings of 10 minutes in duration. This is resting eeg not erp data.</p><p class="yiv9537296241MsoNormal"> </p><p class="yiv9537296241MsoNormal">We wish to analyze these data for two primary questions. First, analyze ic’s and determine sources and compare the groups for differences in regions/sources. What would you suggest using for this? Study function? MPT? Other thoughts? </p><p class="yiv9537296241MsoNormal"> </p><p class="yiv9537296241MsoNormal">Next, we want to measure the difference between the groups in source derived connectivity. We focus on granger causality using PDC as our primary measure. We often use, on an individual level, a SIFT like application that does this. Suggestions would be welcome. Use SIFT for this group level analysis? Other ideas?</p><p class="yiv9537296241MsoNormal"> </p><p class="yiv9537296241MsoNormal">Alternatively, we have thought of using graph theory measures but would prefer to do at the source level not channel. Any thoughts?</p><p class="yiv9537296241MsoNormal"> </p><p class="yiv9537296241MsoNormal">Thanks,</p><p class="yiv9537296241MsoNormal"> </p><p class="yiv9537296241MsoNormal">Rob Coben, PhD </p></div></div></html></div>_______________________________________________<br>Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" target=_blank>http://sccn.ucsd.edu/eeglab/eeglabmail.html</a><br>To unsubscribe, send an empty email to <a ymailto="mailto:eeglablist-unsubscribe@sccn.ucsd.edu" href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu">eeglablist-unsubscribe@sccn.ucsd.edu</a><br>For digest mode, send an email with the subject "set digest mime" to <a ymailto="mailto:eeglablist-request@sccn.ucsd.edu" href="mailto:eeglablist-request@sccn.ucsd.edu">eeglablist-request@sccn.ucsd.edu</a></div></div></div></div></body></html>