<div dir="ltr">Dear Matt,<div><br></div><div><p class="MsoNormal"><b><font color="#0000ff">> Questions</font></b></p><p class="MsoNormal"><font color="#0000ff">Is ICA appropriate for what I want to achieve, or do you suggest other methods of source localisation?</font></p><p class="MsoNormal"><br></p><p class="MsoNormal"><font color="#000000">Technically speaking, ICA is not itself a 3-D source localizer. </font></p><p class="MsoNormal"><font color="#000000">You can use LORETA on ICA-derived scalp topography, for example.</font></p><p class="MsoNormal"><font color="#000000"><br></font></p><p class="MsoNormal"><font color="#000000">For your purpose, ICA can be used. I mean, I can't think of any case where ICA cannot be used... could it be something like, for example, data in which all channels showed complete Gaussian distributions?</font></p><p class="MsoNormal"><font color="#000000"><br></font></p><p class="MsoNormal"><span style="color:rgb(0,0,255)">> Do you have a strategy on how to approach the EEGLAB data-structures on a participant level to access the information I require?</span><font color="#000000"><br></font></p><p class="MsoNormal"><font color="#000000"><br></font></p><p class="MsoNormal"><font color="#000000">Unfortunately, STUDY structure is complicated. When I came to SCCN I could barely retrieve individual subject data from STUDY strucuture. I could still figure it out, without knowing this page,</font><span style="color:rgb(0,0,0)"> and it took me a few days. At the bottom of this wiki page, it says '</span><span style="color:rgb(0,0,0);font-family:sans-serif;font-size:12.7px;line-height:19.05px">This apparently complex scheme...</span><span style="color:rgb(0,0,0)">'</span></p><p class="MsoNormal"><font color="#000000"><br></font></p><p class="MsoNormal"><font color="#000000"><a href="https://sccn.ucsd.edu/wiki/Chapter_07:_EEGLAB_Study_Data_Structures#Understanding_the_.sets.2C_.comps.2C_.setinds.2C_.allinds_substructures_for_STUDY_clusters">https://sccn.ucsd.edu/wiki/Chapter_07:_EEGLAB_Study_Data_Structures#Understanding_the_.sets.2C_.comps.2C_.setinds.2C_.allinds_substructures_for_STUDY_clusters</a></font><br></p><p class="MsoNormal"><br></p><p class="MsoNormal">So your good strategy would be to read the wiki page and efficiently figure out how to retrieve individual data.</p><p class="MsoNormal"><br></p><p class="MsoNormal"><font color="#000000">Makoto</font></p></div><div class="gmail_extra"><br><div class="gmail_quote">On Mon, Sep 19, 2016 at 10:47 PM, Matt Gerhold <span dir="ltr"><<a href="mailto:matt.gerhold@gmail.com" target="_blank">matt.gerhold@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr">
<p class="MsoNormal">ICA gurus:</p>
<p class="MsoNormal">I would be very grateful if you can assist me in understanding
some of the methods and data structures from your toolbox. Specifically, I
would like to gain some clarity regarding working with clusters of independent
components in the source-space, wherein individual participants contribute source-localised
components to a group level cluster.</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal"><b>What I have done and
what I have observed</b></p>
<p class="MsoNormal">I have pre-processed the data (including applying ICA),
performed dipole source localisation, and clustered the components. The
resultant group level clusters make sense: I am using a task that requires that
participants respond with a button press, or alternatively withhold a button
press when cued to do so. I have observed upper alpha (<i>rolandic mu</i>) desynchronization in the component cluster localised
to the motor region contralateral to the button-pressing finger. In addition,
other such event-related spectral features suggest that the clustering and
dipole localisation have been successful.</p>
<p class="MsoNormal">When I inspect the clustered components, I have observed that
some clusters may contain two components from the same participant—the two
components have a similar scalp distribution and spectra leading to similar
clustering. In certain instances, some group level clusters may not have
contributions from one or two participants within the group. Therefore, on the
group level the time-frequency data looks good; however, the observed group
level dynamics do not easily translate into individual participant measures that
can be tabulated for a more rigorous statistical analysis. </p>
<p class="MsoNormal"> </p>
<p class="MsoNormal"><b>What I am trying to
achieve</b></p>
<p class="MsoNormal">I need to check for confounding (a covariate analysis) and
possible mediating effects using a general linear model framework. For this, I
have a collection of measurements for each participant and require a spectral
feature representative of the group level cluster for each participant in order
to perform my analysis. For example, alpha power over the contralateral motor
cortex in a post-stimulus onset window would be a spectral feature; I would
potentially use age-at-recording as a covariate in my analysis to see if
maturational effects explain variation in spectral power over-and-above the
experimental manipulation(s).</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal"><b>Questions</b></p>
<p class="MsoNormal">Is ICA appropriate for what I want to achieve, or do you
suggest other methods of source localisation?</p>
<p class="MsoNormal">Do you have a strategy on how to approach the EEGLAB data-structures
on a participant level to access the information I require?</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">Any advice related to the above questions would be greatly
appreciated. Many thanks in advance.</p>
<p class="MsoNormal">Kind Regards,</p>
<p class="MsoNormal">Matthew</p>
</div>
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</div></div>