<div dir="auto"><div>Dear Hanna,</div><div dir="auto">as to my best knowledge, you can assume the data is stationary in the wide sense for <b>short time periods</b>, e.g. 2 second time frames. </div><div dir="auto">Because of this, one legitimate way we do it in our lab is to cut long resting-state data (e.g. 5 minutes rs experiment) in small overlapping windows (e.g. 2 second time frames with 1 second overlap). These are then your "quasi-trials" you can calculate the covariance matrix on.</div><div dir="auto"><br></div><div dir="auto">I have not applied common filters much but we had this idea too. I also believe it is explained in Jaiswal, Amit, et al. "Comparison of beamformer implementations for MEG source localization." NeuroImage 216 (2020): 116797 in some detail. Somebody else on the list might be more helpful on this.</div><div dir="auto"><br></div><div dir="auto">I hope it helps you somewhat. Don't hesitate to ask any further questions, if I can be of assistance.</div><div dir="auto"><br></div><div dir="auto">Best regards,</div><div dir="auto">Stefan</div><div dir="auto">Research Assistant</div><div dir="auto">Painlab Munich</div><div dir="auto"><br><div class="gmail_quote" dir="auto"><div dir="ltr" class="gmail_attr">On Thu, 24 Feb 2022, 23:15 Honcamp, Hanna (PSYCHOLOGY) via fieldtrip, <<a href="mailto:fieldtrip@science.ru.nl">fieldtrip@science.ru.nl</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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<p>Dear Fieldtrip community, </p>
<p><br>
</p>
<p>I am analyzing continuous EEG resting state (RS) data and I have a couple of questions about the beamformer source reconstruction methods and the best application of it in this context. Firstly, I have not seen many tutorials/papers concerned with RS source
analysis using the lcmv beamforming method. Since I am interested in the analysis and properties of the reconstructed source time courses, the lcmv seemed a viable option. Further, in van Veen et al. (1997), it is described that the computation of the covariance
matrix assumes that the data is “wide sense stationary”. This description seems to be contradictory to the non-stationarity of RS data.
</p>
<p><br>
</p>
<p>Q1: Is the lcmv beamformer the recommended source analysis method for resting state data for the purpose of extracting the source time courses?
</p>
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</p>
<p>Q2: What is the best way to compute the covariance matrix in the context of RS data? Specifically, should I use the whole data or a subset of timepoints? What is the reasoning behind that?<br>
</p>
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</p>
<p>Lastly, I understood that the common filter approach is recommended for within-subject analysis, e.g., comparing conditions. However, it is not clear whether the common filter is also feasible to use in a multi-subject context.
</p>
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<p>Q3: In order to compare the reconstructed source time courses of multiple subjects, do I need to construct a common filter and apply it to all subjects? If so, how does that affect the covariance computation, i.e., should I use all subjects (e.g., in a concatenated
format) for computation of the covariance matrix, or a subset of subjects? </p>
<p><br>
</p>
<p>Many thanks in advance - any advice is much appreciated!</p>
<p>Best, </p>
<p>Hanna<br>
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</p>
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<p>Best, <br>
</p>
<p>Hanna<br>
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</p>
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<div id="m_-1612354974845211002Signature">
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<div><font color="#333333" face="Cambria, serif"><b>Hanna Honcamp</b></font></div>
<font color="#808080" face="Cambria, serif">PhD Candidate | BAND Lab</font>
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<div><font color="#808080" face="Cambria, serif">Faculty of Psychology and Neuroscience</font></div>
<div><font color="#808080" face="Cambria, serif">Dept. NP&PP | Maastricht University</font></div>
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