<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear Jurgen,<div><br></div><div>I had not though of that. </div><div>It makes sense since the power spectral estimates are not linear.</div><div>I guess Marco would have to transform each of his dataset and then build a STUDY, and compute the spectrum.</div><div>Thanks for correcting me,</div><div><br></div><div>Arno</div><div><br></div><div>ps: the code I sent would still be valid for ERPs in STUDY since ERP scalp topographies are a linear transformation.</div><div><br><div><div>On 19 Jan 2013, at 17:39, Craig E. Tenke wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite">
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<div class="moz-cite-prefix">Dear Arno:<br>
<br>
Your advice to Marco on how to use CSD with spectral data is
incorrect. The Laplacian <i>must</i> precede the spectral
computation. If the the spectral transformation is done first, the
result is <i>not </i>an interpretable CSD. The implications of
this error can be quite profound (cf. Fig. 1 of Tenke and Kayser,
2005). As a general rule, a CSD transform can only be applied to
linearly-transformed surface potential data to be physiologically
meaningful, although the underlying algorithm can be applied to
any data, as shown by your example.<br>
<br>
regards from NY,<br>
<br>
Craig Tenke<br>
Jürgen Kayser<br>
<br>
<br>
On 1/18/2013 8:59 PM, Arnaud Delorme wrote:<br>
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<blockquote cite="mid:33AD914F-A5BD-4BA2-B1B0-A00C4F47BD5F@ucsd.edu" type="cite">Dear Marco,
<div><br>
</div>
<div>based on another email I sent earlier to the list.</div>
<div><br>
</div>
<div>
<div><i>% merge all channel location structures for the STUDY</i></div>
<div><i>mergelocs = eeg_mergelocs(ALLEEG.chanlocs);</i></div>
<div><i><span class="Apple-style-span" style="font-style:
normal; "><i> </i></span></i></div>
<div><i>% get ERSP results (scalp maps) - for example scalp
topography of spectrum in the alpha band</i></div>
<div><i>[STUDY spec ] = std_specplot(STUDY,ALLEEG,'channels', {
mergelocs.labels }, 'topofreq', [8 12]);<br>
</i></div>
<div><i><br>
</i></div>
<div>then you may use one of the many laplacian function
eeg_laplac.m is a good one although you will have to rebuild a
dataset to use it. Something like</div>
<div><br>
</div>
<div><i>TMP = EEG(1);</i></div>
<div><i>TMP.chanlocs = mergelocs;</i></div>
<div><i>TMP.data = squeeze(spec{1});</i></div>
<div><i>CSD = eeg_laplac(TMP, 1);</i></div>
<div><br>
</div>
<div><i>% plot average CSD scalp topography</i></div>
<div><i>average_CSD = mean(CSD,2); % average across subjects</i></div>
<div><i>figure; topoplot(average_CSD, mergelocs);</i></div>
<div><br>
</div>
<div>Best,</div>
<div><br>
</div>
<div>Arno</div>
<div><br>
</div>
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<div><br>
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<div>On 17 Jan 2013, at 13:56, Marco Montalto wrote:</div>
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<div>Dear List,<br>
<br>
Is there a way of producing CSD (current source density)
maps in EEGLAB, in a STUDY design?<br>
<br>
Marco Montalto<br>
</div>
</blockquote>
</div>
</div>
</blockquote>
<br>
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