<div dir="ltr">Dear Imali,<div><br></div><div style>In addition to Stan's good advice, you might also find better results by computing the covariance matrix from the unaveraged timelocked data, rather than the averaged ERP data.</div>
<div style><br></div><div style>Cheers,</div><div style>Johanna</div></div><div class="gmail_extra"><br><br><div class="gmail_quote">2013/3/28 Pelt, S. van (Stan) <span dir="ltr"><<a href="mailto:stan.vanpelt@fcdonders.ru.nl" target="_blank">stan.vanpelt@fcdonders.ru.nl</a>></span><br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div style="font-size:12pt;font-family:Times New Roman">Dear Imali,<br><br>What you could do is the following:<br>
1. determine the source of your activation by beamforming your 50ms time-interval data, or by doing a dipole fit on your ERP.<br>2. compute spatial filters at that location based on the covariance matrix of the data of the entire epoch you're interested in (e.g., 500ms pre until 500ms post stimulus onset). You could consider first bandpassing this data for the part of the spectrum that you are interested in (e.g. 13-28Hz for beta).<br>
3. create the virtual waveforms by multiplying the spatial filters with the band-passed data<br><br>Hope that helps.<br><br>Best, Stan<br><br>
--<br>
Stan van Pelt, PhD<br>
Postdoctoral Scientist, Ernst Strüngmann Institute (ESI) for Neuroscience<br>
in Cooperation with Max Planck Society<br>
Deutschordenstr. 46, D-60528 Frankfurt<br>
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<br><hr><blockquote style="border-left:2px solid rgb(16,16,255);margin-left:5px;padding-left:5px"><b>Van: </b>"IMALI THANUJA HETTIARACHCHI" <<a href="mailto:ith@deakin.edu.au" target="_blank">ith@deakin.edu.au</a>><br>
<b>Aan: </b><a href="mailto:fieldtrip@science.ru.nl" target="_blank">fieldtrip@science.ru.nl</a><br><b>Verzonden: </b>Donderdag 28 maart 2013 01:04:34<br><b>Onderwerp: </b>[FieldTrip] Beamformer for ERP data<div><div class="h5">
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<p class="MsoNormal">Dear Fieldtrippers,</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">I am trying to do a LCMV beamformer analysis using the ft_sourceanalysis for a ERP data set. Finally I intend to extract the source waveforms using virtual electrodes.</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">But as we can only do meaningful source localisation for a small time interval say (50ms), how can I construct the source time course for a longer post stimulus period (say 500ms)after doing beamforming for shorter (50ms) time intervals?</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">Many thanks in advance</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">Kind regards</p>
<p class="MsoNormal">Imali</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal"><b><span style="font-family:"Times New Roman","serif"">Imali Thanuja Hettiarachchi</span></b></p>
<p class="MsoNormal"><span style="font-family:"Times New Roman","serif"">PhD Candidate</span></p>
<p class="MsoNormal"><span style="font-family:"Times New Roman","serif"">Centre for Intelligent Systems research</span></p>
<p class="MsoNormal"><span style="font-family:"Times New Roman","serif"">Deakin University,
</span><span style="font-size:10.0pt;font-family:"Times New Roman","serif"">Geelong 3217, Australia.</span></p>
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