<p class="MsoPlainText">Dear Marcel,</p>
<p class="MsoPlainText">My two cents are the following:</p>
<p class="MsoPlainText">I would recommend to compute common filter over ALL
conditions, pre AND post. This will make sure you use as much information as
possible to calculate your cross-spectral density matrix and subsequent filter.
I assume you assume that the location/orientation of the sources do not change
before and after stimulus. Also, in the case that you are looking for a
post-stimulus beta DECREASE it will help you to accurately determine
post-stimulus activity based on a filter calculated from of a period where
there is most (beta) signal.</p>
<p class="MsoPlainText">Then, for your contrast project your conditions, pre and
post *separately* and do your relative contrast then: e.g. projected
post-stimulus activity A / projected pre-stimulus activity A, and same for B.
Then compare relative A - or / by B. </p>
<p class="MsoPlainText">Bottom line, do your common filter on everything, project
and compare everything separately after that.</p>
<p class="MsoPlainText">Hope that helps,</p>
<p class="MsoPlainText"> </p>
<p class="MsoPlainText">Stephen </p><br><div class="gmail_quote">On 24 January 2011 11:44, Bastiaansen, Marcel <span dir="ltr"><<a href="mailto:Marcel.Bastiaansen@mpi.nl">Marcel.Bastiaansen@mpi.nl</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">Dear all,<br>
<br>
I have the following conceptual issue with using beamforming:<br>
<br>
I have data from 2 conditions, A and B. Both contain a prestimulus interval and a poststimulus interval.<br>
<br>
When plotting the TF representations of power, using a *relative* baseline (poststimulus interval / prestimulus interval), a strong difference emerges between the conditions in the beta frequency range. Especially if I plot the *difference between the conditions*, the beta effect is long-lasting, narrow-band and topographically focused. Ideal for beamforming, I'd say.<br>
<br>
In setting up the beamformer analysis, I am faced with the following issue: the effect is most clearly present in the *relative* power changes, so I would want to compute spatial filters for both the pre and the post time intervals. My hunch would be to compute common spatial filters for both pre intervals (preA and preB), and common filters for both post intervals (postA and postB). Then, (after projecting the trials through the filters), compute the relative power again (pre/post for each condition). Does this approach make sense?<br>
<br>
Next question is then, at which point in the analysis pipeline should one do the condition subtraction? (I'd want to do that because at the TFR level the effect comes out most clearly in the condition difference)<br>
<br>
<br>
Best,<br>
Marcel<br>
<br>
<br>
<br>
-- <br>
dr. Marcel C.M. Bastiaansen.<br>
<br>
Max Planck Institute for Psycholinguistics<br>
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<br>
and<br>
<br>
Donders Institute for Brain, Cognition and Behaviour<br>
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