<div dir="ltr">If you plan to take the absolute value of the data (or the norm of the three components) it's advisable to equalize the event counts. This nonlinear operation takes what is typically thought of as being zero-mean, normally distributed noise, and turns it into a folded normal distribution. A folded normal distribution has a mean that is dependent on the standard deviation of the un-folded version of the distribution. Different trial counts yield different effective noise standard deviations, which then become different noise *means* once the distribution gets folded by the abs()-like operation.<div><br></div><div>In other words, if you don't equalize the trial counts before a nonlinear norm operation, you can end up with potentially biased estimates. At one point I tried correcting for this bias, but was unsuccessful -- the noise distribution, even if turned zero-mean, was skewed. Perhaps there is some good way to deal with it, but subsampling the data (equalizing trial counts) has seemed to be the safest thus far. It's probably not a big deal if it's 35 vs 40 trials, but if you have an unbalanced design of e.g. 30 trials in one condition and 60 trials in another, it's more likely to matter.<div><br></div><div>Eric</div><div><br></div></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Thu, Mar 3, 2016 at 11:02 AM, Laetitia Grabot <span dir="ltr"><<a href="mailto:laetitia.grabot@gmail.com" target="_blank">laetitia.grabot@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Ok, thanks, I was still applying the old advice of equalizing epochs between conditions! But so, why in certain statistical examples of the website, you're still equalizing conditions (like in the Reapeated-measures ANOVA in sources)?</div><div class="HOEnZb"><div class="h5"><div class="gmail_extra"><br><div class="gmail_quote">2016-03-03 16:43 GMT+01:00 Denis-Alexander Engemann <span dir="ltr"><<a href="mailto:denis.engemann@gmail.com" target="_blank">denis.engemann@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Yes that's the idea. Amplitudes of the inverse solution are scaled by the number of trials (.nave). This should work as an heuristic unless you compare rare to frequent events (e.g. oddball).<div>Hope this helps.</div></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">On Thu, Mar 3, 2016 at 4:38 PM, Laetitia Grabot <span dir="ltr"><<a href="mailto:laetitia.grabot@gmail.com" target="_blank">laetitia.grabot@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">So does that mean that we need not to equalize epochs between conditions (within one subject)?</div><div><div><div class="gmail_extra"><br><div class="gmail_quote">2016-03-03 16:23 GMT+01:00 Denis-Alexander Engemann <span dir="ltr"><<a href="mailto:denis.engemann@gmail.com" target="_blank">denis.engemann@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hi Laetitia, <div><br></div><div>I I'm understanding you correctly this should not be an issue for evoked data as long as you are using the .average method that will tell the inverse routines how to scale the data via its .nave attribute.</div><div>Did you have any particular problems?</div><div><br></div><div>Cheers,</div><div>Denis</div></div><div class="gmail_extra"><br><div class="gmail_quote"><div><div>On Thu, Mar 3, 2016 at 3:09 PM, Laetitia Grabot <span dir="ltr"><<a href="mailto:laetitia.grabot@gmail.com" target="_blank">laetitia.grabot@gmail.com</a>></span> wrote:<br></div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div><div dir="ltr">Dear MNE-users,<div><br><div>I am aware of the fact that when contrasting two conditions, the epochs within each condition should be equalized before reconstructing in source space, otherwise the SNR would be different between the two conditions. That also means that if two subjects have a different number of epochs, the SNR of each subject will be different. It is often the case that the number of epochs doubled between subjects (50 vs. 100) so I guess that it is an issue when looking at group averaged stc for instance.</div></div><div>A possible solution would be to normalize the stc to correct for the number of trials. What do you think of that? What would be the proper normalization? </div><div><br></div><div>Thanks,</div><div>Best,</div><div>Laetitia G.</div></div>
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