[FieldTrip] One-sample cluster based permutation t-test ERP data

Maris, E.G.G. (Eric) e.maris at donders.ru.nl
Sun Aug 19 23:57:32 CEST 2018

Dear FT-ers,

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:

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.

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.

Eric Maris

  1. Re: One-sample cluster based permutation t-test ERP data
     (Stephen Politzer-Ahles)

From: Stephen Politzer-Ahles <politzerahless at gmail.com<mailto:politzerahless at gmail.com>>
Subject: Re: [FieldTrip] One-sample cluster based permutation t-test ERP data
Date: 19 August 2018 at 10:36:09 CEST
To: <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>

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.

Stephen Politzer-Ahles
The Hong Kong Polytechnic University
Department of Chinese and Bilingual Studies


Message: 1
Date: Fri, 17 Aug 2018 16:01:29 +0000
From: Alex Sel <alex.sel at psy.ox.ac.uk<mailto:alex.sel at psy.ox.ac.uk>>
To: "fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>" <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Subject: [FieldTrip] One-sample cluster based permutation t-test ERP
Message-ID: <47637187-A1A5-4040-8917-F04C501E65CF at psy.ox.ac.uk<mailto:47637187-A1A5-4040-8917-F04C501E65CF at psy.ox.ac.uk>>
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Dear list,

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.

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.

Any help would be appreciated.

Best wishes,

Alex Sel, PhD
Associate Fellow of the Higher Education Academy
Wellcome Centre for Integrative Neuroimaging
Department of Experimental Psychology
University of Oxford
The Tinsley Building

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Eric Maris | Donders Institute for Brain, Cognition, and Behaviour & Faculty of Social Sciences | Radboud University | PO Box 9104, 6500 HE Nijmegen | (024) 3612651 | www.ru.nl<http://www.ru.nl>

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