<div dir="ltr"><div style>Dear Jan-Mathijs</div><div><br></div>Thank you for the quick response. Computing the covariance seems to be working fine now.<div><br></div><div style>I'm curious what you would suggest for computing the differential power?</div>
<div style><br></div><div style>at the moment I'm not keeping any trial measures (cfg.keeptrials='no') in either ft_timelockanalysis or ft_sourceanalysis. I'm then computing a psuedoT as</div><div style><br>
</div><div style><div>act_sourceAnalysis.avg.pseudoT = (act_sourceAnalysis.avg.pow - cnt_sourceAnalysis.avg.pow)./(act_sourceAnalysis.avg.noise + cnt_sourceAnalysis.avg.noise);</div><div><br></div><div style>I wanted to compare this pseudoT to a standard T score.</div>
<div style><br></div><div style>I can get trial by trial power estimates by using cfg.keeptrials='yes' in ft_timelockanalysis and cfg.keeptrials='yes' and ctf.rawtrial='yes' in ft_sourceanalysis. but ft_sourcestatistics doesn't seem to let me do a paired T the way I would hope. Is there an alternative route that you might take or is there another method that could be useful?</div>
<div style><br></div><div style>Thanks,</div><div style>Luke</div><div style><br></div><div style><br></div><div style><br></div><div style><br></div><div><br></div></div></div><div class="gmail_extra"><br><br><div class="gmail_quote">
On Thu, Apr 17, 2014 at 4:27 AM, jan-mathijs schoffelen <span dir="ltr"><<a href="mailto:jan.schoffelen@donders.ru.nl" target="_blank">jan.schoffelen@donders.ru.nl</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div style="word-wrap:break-word">Hi Luke,<div><br><div><div class=""><div>On Apr 16, 2014, at 9:29 PM, Luke Bloy wrote:</div><br><blockquote type="cite"><div dir="ltr">Hi All,<div><br></div><div>I'm interested in using lcmv to do a common mode differential beamformer. My understanding, please correct me if I'm incorrect, is that the best way forward would be to</div>
<div>1) compute a covariance matrix from a time window combining both active and control windows.</div><div>2) use this combined covariance to make a common mode lcmv beamformer</div><div>3) apply this to both active and control windows separately</div>
<div>4) combine into some sort of psuedo T or NAI.</div><div><br></div><div>does this sound correct?</div></div></blockquote><div><br></div></div><div>Yes it does.</div><div class=""><div><br></div><blockquote type="cite">
<div dir="ltr"><div>If so the main thing I'm unclear on is how to compute the covariance for 2 distinct time intervals? is this possible?</div></div></blockquote><div><br></div></div><div>What you could do is call ft_timelockanalysis twice: each time with a different cfg.covariancewindow. Assuming you have an equal number of trials for both time windows, averaging the two covariance matrices will give you the combined covariance matrix.</div>
<div><br></div><div>Best,</div><div>Jan-Mathijs</div><div><br></div><br><blockquote type="cite"><div dir="ltr">
<div><br></div><div>Thanks,</div><div>Luke</div></div>
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<div>Jan-Mathijs Schoffelen, MD PhD </div><div><br></div><div>Donders Institute for Brain, Cognition and Behaviour, <br>Centre for Cognitive Neuroimaging,<br>Radboud University Nijmegen, The Netherlands</div><div><br></div>
<div>Max Planck Institute for Psycholinguistics,</div><div>Nijmegen, The Netherlands</div><div><br></div><div><a href="mailto:J.Schoffelen@donders.ru.nl" target="_blank">J.Schoffelen@donders.ru.nl</a></div><div>Telephone: <a href="tel:%2B31-24-3614793" value="+31243614793" target="_blank">+31-24-3614793</a></div>
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