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<div>Dear FT-ers,</div>
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<div>The one-sample statistical test continues to be an issue that raises confusion. Although it is impossible to explain here the statistical background in all detail, the following points are relevant for empirical neuroscientists that apply cluster-based
permutation tests to their data:</div>
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<div>1. A permutation test can only be used for comparing two or more experimental conditions. Thus, Stephen’s proposal is not a permutation test. However, it does produce a p-value, and by comparing it with some nominal value (e.g., 0.05) it can be used to
take a decision. The problem with this procedure is that it is unclear what is the null hypothesis to which this decision pertains. Here lies the important difference with a permutation test for the difference between two experimental conditions: tthe null
hypothesis involves that the biological data in the two conditions are generated by the same probability distribution.</div>
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<div>2. Comparing the activation (post-stimulus) with the baseline (pre-stimulus) period can be performed using a permutation test, regardless whether the raw data were transformed into a time-frequency representation or not. The reason why some people think
it cannot be used on the raw data (i.e., for testing effects on the time-locked average) is that the baseline period is typically used to normalize the activation period (by removing the DC component). With this normalisation, the null hypothesis pertains
to two sets of biological data of which one (the activation data) is already a function of the other (as a result of the normalisation procedure). If you want to use the actvsblT statfun, It makes more sense to perform a high-pass filter on the raw data rather
than normalising the activation data using the baseline data.</div>
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<div>best,</div>
<div>Eric Maris</div>
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<div class=""> 1. Re: One-sample cluster based permutation t-test ERP data<br class="">
(Stephen Politzer-Ahles)<br class="">
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<span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(127, 127, 127, 1.0);" class=""><b class="">From:
</b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class="">Stephen Politzer-Ahles <<a href="mailto:politzerahless@gmail.com" class="">politzerahless@gmail.com</a>><br class="">
</span></div>
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<span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(127, 127, 127, 1.0);" class=""><b class="">Subject:
</b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class=""><b class="">Re: [FieldTrip] One-sample cluster based permutation t-test ERP data</b><br class="">
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<span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(127, 127, 127, 1.0);" class=""><b class="">Date:
</b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class="">19 August 2018 at 10:36:09 CEST<br class="">
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<span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(127, 127, 127, 1.0);" class=""><b class="">To:
</b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class=""><<a href="mailto:fieldtrip@science.ru.nl" class="">fieldtrip@science.ru.nl</a>><br class="">
</span></div>
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<div dir="ltr" class="">I'm not sure if there is or not, but in the past I have managed this by creating an ERP dataset that is all zeroes, and then comparing my real dataset to it with a cluster test. Since a one-sample test is the same as a paired samples
test between something and zero (e.g., comparing X and Y via t-test is the same as comparing {X-Y} to zero), this should give the same result.
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Stephen Politzer-Ahles<br class="">
The Hong Kong Polytechnic University<br class="">
Department of Chinese and Bilingual Studies<br class="">
<a href="http://www.mypolyuweb.hk/~sjpolit/" target="_blank" class="">http://www.mypolyuweb.hk/~sjpolit/</a></span><a href="http://www.nyu.edu/projects/politzer-ahles/" target="_blank" class=""></a></div>
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------------------------------<wbr class="">------------------------------<wbr class="">----------<br class="">
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Message: 1<br class="">
Date: Fri, 17 Aug 2018 16:01:29 +0000<br class="">
From: Alex Sel <<a href="mailto:alex.sel@psy.ox.ac.uk" class="">alex.sel@psy.ox.ac.uk</a>><br class="">
To: "<a href="mailto:fieldtrip@science.ru.nl" class="">fieldtrip@science.ru.nl</a>" <<a href="mailto:fieldtrip@science.ru.nl" class="">fieldtrip@science.ru.nl</a>><br class="">
Subject: [FieldTrip] One-sample cluster based permutation t-test ERP<br class="">
data<br class="">
Message-ID: <<a href="mailto:47637187-A1A5-4040-8917-F04C501E65CF@psy.ox.ac.uk" class="">47637187-A1A5-4040-8917-<wbr class="">F04C501E65CF@psy.ox.ac.uk</a>><br class="">
Content-Type: text/plain; charset="utf-8"<br class="">
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Dear list,<br class="">
<br class="">
I wonder if there has been a function developed to do a one-sample cluster based permutation t-test ERP data. I am aware this is possible to do with actvsblT for time-frequency data.
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There is a forum thread from 2012 saying that this wasn’t implemented. But I wonder if there is anyone there who might have resolve this issue and wouldn’t mind sharing the solution.<br class="">
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Any help would be appreciated. <br class="">
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Best wishes,<br class="">
<br class="">
Alex Sel, PhD<br class="">
Associate Fellow of the Higher Education Academy <br class="">
Wellcome Centre for Integrative Neuroimaging <br class="">
Department of Experimental Psychology <br class="">
University of Oxford <br class="">
The Tinsley Building <br class="">
OX1 3SR<br class="">
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https://doi.org/10.1371/journal.pcbi.1002202<br class="">
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<font face="Calibri" class=""><span style="font-size: 11px;" class="">Eric Maris | Donders Institute for Brain, Cognition, and Behaviour & Faculty of Social Sciences | Radboud University | PO Box 9104, 6500 HE Nijmegen | (024) 3612651 | <a href="http://www.ru.nl" class="">www.ru.nl</a><br class="">
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