<html><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear Dahlia,<div><br></div><div>Hemant Bokil indeed uses the jackknife to obtain variance estimates of power (and coherence), see also for example his 2007 paper in J.Neurosci.Methods. More specifically, if your data is 'well-behaved', he has shown that by applying a specific correction to the power estimate, in combination of the jackknife, generates a test-statistic from a differential (i.e. contrast) power spectrum which has a standard normal distribution (i.e. the jackknife estimate of the variance is expected to be 1 and the estimate of the mean 0). Unfortunately, MEG data is hardly ever well-behaved, so we prefer to use non-parametric techniques to do statistical inference.</div><div>Freqdescriptives in this respect still historically has the option of computing a jackknife estimate of the SEM of the powerspectrum/coherencespectrum, which can be used to compute a T-statistic across two conditions for example. However, I you would choose this path, you have to write some code which does this, because it is not in fieldtrip. The biascorrect option has been taken out altogether as far as I can see, (and had been designed only to correct the bias in the coherence spectra, and not in the power spectra if I remember correctly), and any reference in the documentation should be removed. Unfortunately, we did not yet have time to considerably clean up freqdescriptives, but this is quite high on the developer's to do list.</div><div>The bottom line is: ignore biascorrect, and if you use the jackknife estimate of the SEM, you have to come up with some code of your own. Alternatively, you could look into freqstatistics and use cfg.statistic = 'indepsamplesT' / 'depsamplesT' if you want to do statistical inference.</div><div><br></div><div>Best,</div><div><br></div><div>Jan-Mathijs</div><div><br></div><div><br><div><div>On 28 Aug 2009, at 06:34, Dahlia Sharon wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><div><div style="font-family: Arial; font-size: 12pt; color: rgb(0, 0, 0); ">Hi all,<br><br>Is there a more detailed explanation of usage for the jackknife and biascorrect options for freqdescriptives than the one in the freqdescriptives reference page(<a href="http://fieldtrip.fcdonders.nl/reference/freqdescriptives)?">http://fieldtrip.fcdonders.nl/reference/freqdescriptives)?</a><br><br>More specifically, are these options related to Bokil et al NeuroIm 2007? How should they be employed to determine significance of difference between conditions? (Is there somewhere a tutorial for the use of these options analogous to the one about cluster-based permutation testing?)<br><br>Also, for the permutation analysis of TFRs (<a href="http://fieldtrip.fcdonders.nl/tutorial/statistics?s">http://fieldtrip.fcdonders.nl/tutorial/statistics?s</a>[]=freqstatistics), if I don't want to employ the planar gradient step (what exactly IS combineplanar? sorry I couldn't find it), can I simply skip it and calculate the TFRs of the raw sensor data?<br><br>Many thanks!<span class="Apple-converted-space"> </span><br>Dahlia.<br></div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">----------------------------------</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">The aim of this list is to facilitate the discussion between users of the FieldTrip toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis.</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; "><a href="http://listserv.surfnet.nl/archives/fieldtrip.html">http://listserv.surfnet.nl/archives/fieldtrip.html</a></div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; "><a href="http://www.ru.nl/fcdonders/fieldtrip/">http://www.ru.nl/fcdonders/fieldtrip/</a></div></div></span></blockquote></div><br></div></div><p>----------------------------------</p><p>The aim of this list is to facilitate the discussion between users of the FieldTrip toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis.</p><p> <a href="http://listserv.surfnet.nl/archives/fieldtrip.html">http://listserv.surfnet.nl/archives/fieldtrip.html</a></p><p> <a href="http://www.ru.nl/fcdonders/fieldtrip/">http://www.ru.nl/fcdonders/fieldtrip/</a></p> <br><br></div><br></body></html><p>----------------------------------</p>
<p>The aim of this list is to facilitate the discussion between users of the FieldTrip toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis.</p>
<p> http://listserv.surfnet.nl/archives/fieldtrip.html</p>
<p> http://www.ru.nl/fcdonders/fieldtrip/</p>