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<span style="line-height:150%"><font size="2">Hi Ana,</font></span></p>
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<font size="2"><span style="line-height:150%"> The single subject approach and the group approach have their respective supporters.
</span><span style="font-family:Tahoma,sans-serif">To sum up:</span><span style="line-height:150%"></span></font></p>
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<font size="2"><span style="line-height:150%"> </span><span style="line-height:150%"></span></font></p>
<p class="MsoNormal" style="margin-bottom:6.45pt; line-height:150%"><span style="line-height:150%"><font size="2">• Single Subject Approach:</font></span></p>
<p class="MsoNormal" style="margin-bottom:6.45pt; line-height:150%"><span style="line-height:150%"><font size="2">- First, individual ICA decomposition (e.g., 128 ICs by subjects for a 128 channel recording)<br>
- Then, group analysis: clustering of similar ICs based on various criteria: similarity of scalp maps, ERPs, dipole locations, etc.<br>
- Potential difficulties: Processing time (</font></span><span style="font-size:small; line-height:150%">data-overload?)</span><span style="font-size:small; line-height:150%">, choice of the number of clusters and method of clustering.</span></p>
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<span style="line-height:150%"><font size="2"> References: all the main EEGLAB papers and tutorials: </font></span><a href="http://sccn.ucsd.edu/eeglab/eeglabtut.html" style="line-height:150%; font-size:10pt">http://sccn.ucsd.edu/eeglab/eeglabtut.html</a></p>
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<span style="font-size:small; line-height:150%"> </span><span style="font-size:small; line-height:150%"> </span></p>
<p class="MsoNormal" style="margin-bottom:6.45pt; line-height:150%"><span style="line-height:150%"><font size="2">• Group Approach:</font></span></p>
<p class="MsoNormal" style="margin-bottom:6.45pt; line-height:150%"><span style="line-height:150%"><font size="2">- First, concatenation of all subjects for each condition<br>
- Then single ICA<br>
- Main Problem: dealing with inter-individual differences in brain anatomy/physiology ("my Cz is not your Cz")<br>
People often run PCA first and retain the largest factors before ICA… which may result in the same difficulty for choosing the number of factors</font></span></p>
<p class="MsoNormal" style="margin-bottom:6.45pt; line-height:150%"><span style="line-height:150%"><font size="2">References:<br>
Dien et al., (2007 in Human Brain Mapping)<br>
Eichele et al., (2008, 2009 in International Journal of Psychophysiology): parallel EEG-fMRI ICA, see also their “Group ICA of EEG Toolbox” (EEGIFT):</font></span></p>
<a href="http://icatb.sourceforge.net/groupica.htm" target="_blank"><font size="2">http://icatb.sourceforge.net/groupica.htm</font></a>
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<p class="MsoNormal" style="margin-bottom:0.0001pt; line-height:150%">Best,</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt; line-height:150%">Louis Renoult</p>
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<div id="divRpF586015" style="direction:ltr"><font face="Tahoma" size="2" color="#000000"><b>From:</b> eeglablist-bounces@sccn.ucsd.edu [eeglablist-bounces@sccn.ucsd.edu] on behalf of Ana Navarro Cebrian [anavarrocebrian@gmail.com]<br>
<b>Sent:</b> Wednesday, April 10, 2013 7:35 PM<br>
<b>To:</b> eeglablist@sccn.ucsd.edu<br>
<b>Subject:</b> [Eeglablist] ICA of concatenated subjects?<br>
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<div dir="ltr"><span style="border-collapse:collapse; font-family:arial,sans-serif; font-size:13px">Hi,
<div>I was wondering if there is any problem with trying ICA for the entire group of subjects (as it's usually done in fMRI) instead of running ICA for each individual subject and then finding the common components with a cluster analysis. I thought it could
be as simple as concatenating all the subject's data, but I tried it and the ICA components that I got from it are looking quite bad. They look as if the data were really noisy. I understand that there is more variability, but this data is very clean and I
expected something better. Am I missing something? I appreciate any help.</div>
<div>Many thanks,</div>
<div>Ana</div>
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