<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="">Hi David,<div class=""><br class=""></div><div class="">regarding the lambda, I think there are different ideas floating around the fieldtrip discussion-list. I suggest searching for the term „lambda“ to get a rough idea. Personally, for our EEG-data I usually use 10%.</div><div class=""><br class=""></div><div class="">What is your question exactly regarding the covariance as input?</div><div class=""><br class=""></div><div class="">Cheers,</div><div class=""><br class=""></div><div class="">Julian</div><div class=""><br class=""><div><blockquote type="cite" class=""><div class="">Am 13.09.2017 um 13:22 schrieb David Pascucci <<a href="mailto:psc.dav@gmail.com" class="">psc.dav@gmail.com</a>>:</div><br class="Apple-interchange-newline"><div class=""><div dir="ltr" class="">Thaks Julian,<div class="">that is the approach I was using, with eLoreta.</div><div class="">I am not sure about two steps,though.</div><div class="">One is the estimate and use of the signal covariance to input for <span style="font-size:12.8px" class="">single-trial activity in source space.</span></div><div class=""><span style="font-size:12.8px" class="">The other is the choice of the optimal lambda.</span></div><div class=""><span style="font-size:12.8px" class=""><br class=""></span></div><div class=""><span style="font-size:12.8px" class="">If you have some advice, that wold be very helpful.</span></div><div class=""><span style="font-size:12.8px" class=""><br class=""></span></div><div class=""><span style="font-size:12.8px" class="">Thanks,</span></div><div class=""><span style="font-size:12.8px" class="">David</span></div></div><div class="gmail_extra"><br class=""><div class="gmail_quote">2017-09-13 12:22 GMT+02:00 Julian Keil <span dir="ltr" class=""><<a href="mailto:julian.keil@gmail.com" target="_blank" class="">julian.keil@gmail.com</a>></span>:<br class=""><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div style="word-wrap:break-word" class="">Hi David,<div class=""><br class=""></div><div class="">do you want to obtain single-trial activity in source space? In that case, have you looked at the „virtual sensors“-tutorial? <a href="http://www.fieldtriptoolbox.org/tutorial/shared/virtual_sensors" target="_blank" class="">http://www.<wbr class="">fieldtriptoolbox.org/tutorial/<wbr class="">shared/virtual_sensors</a></div><div class="">In the tutorial, LCMV is used for the source analysis, but it should also work with sloreta, as the output-structure of the source-analysis is identical. I’m not sure about MNE though.</div><div class=""><br class=""></div><div class="">Good luck,</div><div class=""><br class=""></div><div class="">Julian</div><div class=""><br class=""></div><div class=""><br class=""><div class=""><blockquote type="cite" class=""><div class=""><div class="h5"><div class="">Am 12.09.2017 um 20:47 schrieb David Pascucci <<a href="mailto:psc.dav@gmail.com" target="_blank" class="">psc.dav@gmail.com</a>>:</div><br class="m_572360733086510478Apple-interchange-newline"></div></div><div class=""><div class=""><div class="h5"><div dir="ltr" class=""><p class="m_572360733086510478gmail-p1"><span class="m_572360733086510478gmail-s1">Dear fieldtrip experts,</span></p><p class="m_572360733086510478gmail-p1"><span class="m_572360733086510478gmail-s1">I was wondering if anyone has experience with extracting single trials estimates of source activity (using MNE or Loreta-based approaches) from regions of interest, and what would be the best procedure…</span></p><p class="m_572360733086510478gmail-p2"><span class="m_572360733086510478gmail-s1"></span><br class=""></p><p class="m_572360733086510478gmail-p1"><span class="m_572360733086510478gmail-s1">Thanks in advance</span></p></div></div></div>
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