<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><div class="">Dear Britta,</div><div class=""><br class=""></div><div class="">Thank you for your answer. I am no expert into the algorithms. To compute data covariance matrix, I guess the time window is one dimension that could be used as the data samples, but trial number could also play the role in the other dimension. By select a time window, a common spatial filter would be created that representing all the data within the time window. My question is: will this kind of spatial filter make the source output temporally smoothed or "affected"? (For it collapses the data sample in the time dimension) Especially when there are more than one sources in the time window of interest.</div><div class=""><br class=""></div><div class="">Best,</div><div class="">Hao</div><br class=""><div><blockquote type="cite" class=""><div class="">On Jul 5, 2017, at 5:36 PM, Britta Westner <<a href="mailto:britta.wstnr@gmail.com" class="">britta.wstnr@gmail.com</a>> wrote:</div><br class="Apple-interchange-newline"><div class="">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" class="">
<div text="#000000" bgcolor="#FFFFFF" class=""><p class="">Dear Hao,</p><p class="">the time window is needed to estimate the data covariance matrix,
one of the "ingredients" for calculating the LCMV beamformer
spatial filter that will be applied to your sensor space data.
Generally, the estimate of this covariance matrix is better with
more data samples. Thus, spatial filters constructed on small
snippets of your data will be less reliable than spatial filters
constructed on a longer time window.<br class="">
Furthermore, if you use several time windows, i.e., several
filters, I suspect that this can potentially lead to
discontinuities in your source time series (if you intend to glue
the output of your beamformers together).<br class="">
Usually, to construct your data covariance matrix, you would use a
time window of interest, representing the activity you are
interested in. <br class="">
</p><p class="">I hope this helps,<br class="">
Cheers,<br class="">
Britta<br class="">
</p><p class=""><br class="">
</p><p class=""><br class="">
</p>
Am 05.07.2017 um 10:13 schrieb
<a class="moz-txt-link-abbreviated" href="mailto:mne_analysis-request@nmr.mgh.harvard.edu">mne_analysis-request@nmr.mgh.harvard.edu</a>:<br class="">
<br class="">
<blockquote type="cite" cite="mid:CADeotZob+wQCReRG1kgOGuoo-Dq5Mabj-WUuY_+27m-bxidg0g@mail.gmail.com" class="">
<pre wrap="" class="">From: jehherson chow <a class="moz-txt-link-rfc2396E" href="mailto:jehherson@gmail.com"><jehherson@gmail.com></a>
Date: Wed, Jul 5, 2017 at 10:13 AM
Subject: [Mne_analysis] mne beamformer lcmv
To: <a class="moz-txt-link-rfc2396E" href="mailto:mne_analysis@nmr.mgh.harvard.edu">"mne_analysis@nmr.mgh.harvard.edu"</a> <a class="moz-txt-link-rfc2396E" href="mailto:mne_analysis@nmr.mgh.harvard.edu"><mne_analysis@nmr.mgh.harvard.edu></a>
Dear MNE Experts,
I am using mne lcmv to reconstruct the sources. I find in mne I can
apply lcmv beamformer on evoked data with a common spatial filter of
all experimental conditions and the entire time window (e.g. from 0ms
to 1000ms). The results look nice, but the method seems to be
skeptical. It seems that it’s better to use a moving window instead of
the entire window, which means that a moving window is kind of
preserving the temporal resolution, while the entire time window
sacrifice the temporal resolution. But the problematic thing of the
moving window method is that it requires more than one spatial filter
and the difference between these filters might make a confounding
output. Do you know which the right way to do beamforming?
Best,
Hao</pre>
</blockquote>
<br class="">
<br class="">
</div>
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