<html><head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">Hi Lars,<div class=""><br class=""></div><div class="">we recently faced a similar problem (see here: <a href="https://www.nature.com/articles/s41598-019-42380-x" class="">https://www.nature.com/articles/s41598-019-42380-x</a>). Our solution was to create „dummy“ data based on the actual data and use these in the comparison.</div><div class="">In short, this relates to Eric Maris’ statement about the null hypothesis: In our case, the H0 was „the regression weights are independent of the stimulus category“, so we test our empirically found regression weights against random regression weights. Please note that we don’t have a formal proof that his is a valid approach.</div><div class=""><br class=""></div><div class="">Unfortunately, I don’t think this is directly applicable to your situation, as it sounds like you only have one category, but maybe there is a way for you to come up with dummy data? </div><div class="">Again, please note that there might be problems with our approach we didn’t consider.</div><div class=""><br class=""></div><div class="">Best,</div><div class=""><br class=""></div><div class="">Julian</div><div class=""><br class=""><div><br class=""><blockquote type="cite" class=""><div class="">Am 02.05.2019 um 12:05 schrieb Lars Costers <<a href="mailto:larscosters@gmail.com" class="">larscosters@gmail.com</a>>:</div><br class="Apple-interchange-newline"><div class=""><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><span class="">Hi all,</span><span class=""><br class=""><br class=""></span><span class="">I was wondering whether there are any one-sample permutation statistics to test for a difference from zero implemented in Fieldtrip, ideally to work with monte-carlo permutations? Didn’t find any standard of 'statfun’ options in the documentation.<br class=""></span><span class=""><br class=""></span><span class="">My goal is to do maxstat correction on ERF MEG data over voxel space.<br class=""></span><span class="">cfg.latency = [-0.2 0.8];<br class="">cfg.parameter = 'avg';<br class="">cfg.method = 'montecarlo';<br class="">cfg.numrandomization = 2000;<br class="">cfg.correctm = 'max'; % MaxStat correction<br class="">cfg.design = ones(1,nsub);<br class="">cfg.ivar = 1; % independent variable<br class="">cfg.statistic = ???;<br class="">[stat] = ft_timelockstatistics (cfg, timelock{:,1});<br class=""><br class=""> I read the discussions about one-sample cluster-based permutation tests (e.g. <a href="https://mailman.science.ru.nl/pipermail/fieldtrip/2018-August/012314.html" class="">https://mailman.science.ru.nl/pipermail/fieldtrip/2018-August/012314.html</a></span>). However, for me it's not possible to permute the baseline and post stimulus condition because I have expectation reactions in the baseline. <div class=""><span class="">Anybody could suggest me how I could validly test whether my ERF is signficantly different from zero at every time-frame and electrode? <br class=""><br class=""></span><span class="">Thanks, <br class=""></span><span class=""><br class=""></span><span class="">Lars</span></div></div>_______________________________________________<br class="">fieldtrip mailing list<br class=""><a href="https://mailman.science.ru.nl/mailman/listinfo/fieldtrip" class="">https://mailman.science.ru.nl/mailman/listinfo/fieldtrip</a><br class="">https://doi.org/10.1371/journal.pcbi.1002202<br class=""></div></blockquote></div><br class=""></div></body></html>