Enrico - <br><br>A best solution would be to record from more electrodes <font>at frontal and inferior electrodes around the head -- since (muscle) source density is higher here, electrode density should best be higher here as well (as non-intuitive as that may sound).</font> This is particularly relevant for a gamma band study (see Onton & Makeig, 2009 for an example with 256 electrodes over the whole scalp). <br>
<br>Also, you should investigate using Amica (Palmer, 2007) with unlikely-data rejection set on, and/or possibly using two or more models competing for the data (though the latter option is still difficult to interpret). Jason Palmer is about to release binaries for PC, Mac, and linux that take advantage of multiple cores and processors when possible. A study in press (Delorme et al., PLoS One, 2/3/12-) shows Amica to be the best algorithm for blind source decomposition of EEG data from at least two angles...<br>
<br>Multi-subject ICA sacrifices much of the specificity and accuracy of ICA, and also suffers more from undercompleteness (e.g., more distinct-if-overlapping independent source projections than channels) than single-subject data.... <br>
<br>Scott Makeig<br><br><div class="gmail_quote">On Wed, Feb 1, 2012 at 6:44 AM, Enrico Schulz <span dir="ltr"><<a href="mailto:enrico.schulz@gmail.com">enrico.schulz@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><font>Dear EEGlab list,</font><font><br></font></div><div><font><br></font></div><div><font>I have a problem with the ICA-based artefact reduction that is actually not just restricted to the EEGlab software.</font></div>
<div><font><br></font></div><div><font>I'm struggling with </font><font>a lot of high frequency- artefacts at frontal and inferior electrodes around the head exhibiting a much higher amplitude than the cortical gamma band activity I'm interested in. </font><font>Although it is possible to remove the strongest artefacts, some muscle activity could not be removed in my data sets because some of the artefacts do not give rise to a separate component.</font></div>
<div><font><br></font></div><div><font>In my naive view, in addition to the fact that there are still artefacts in the data set, this could lead to a bias for some subjects. In theory, if a strong artefact gives rise to an independent component and can, hence, be removed, the amount of artefacts in that data set is now lower than in a different data set, where that artefact is not strong enough for a distinct component.</font></div>
<div><font><br></font></div><div><font>The problem is even more complicated if an experimental group (e.g. pain patients) has stronger muscle artefacts than a healthy control group.</font></div><div><font><br></font></div>
<div><font>Sorry for the long introduction, but my actual question is, whether it is possible to concatenate all single subject files and doing the ICA for that big file.</font></div><div><font>I'm aware that this approach has other disadvantages, e.g. it requires a similar topography for each artefact across all subjects and a fast machine. <br>
</font></div><div><font><br></font></div><div><font>Any help/opinion is highly appreciated!<br></font></div><div><font><br></font></div><div><font>Best regards,<br></font></div><div><font>Enrico<br></font></div><div><br>
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