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

Tzvetomir Tzvetanov tzvetomir.tzvetanov at gmail.com
Fri Aug 24 11:23:26 CEST 2018

I resent my response in case people may be interested.


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Re: [FieldTrip] One-sample cluster based permutation t-test ERP data
Tzvetomir Tzvetanov <tzvetomir.tzvetanov at gmail.com>
18/08/21 13:14

fieldtrip at science.ru.nl, alex.sel at psy.ox.ac.uk, politzerahless at gmail.com

Dear Alex, Dear Stephen, Dear Eelke, Dear all,

we have worked on such a case, that is trying to test a single sample.

We made an arxiv publication, after rejection from J. of Neuroscience 
Methods with a similar argument presented by Dr. Eric Maris at that time 
too (a correct open review).

We decided to make an arxiv publication and leave the community decide 
if our approach is worth. (https://arxiv.org/abs/1801.09372)

For the moment I do think single sample test is available. It can be 
traced (a very late discovery from my side) also to the non-linear 
dynamics literature where it is called surrogate data 
analysis/statistics. I found a good description is the book from Kantz 
or his more recent reviews (non-linear time series analysis).

Hope this gives some clues for people wondering about this open question 
in our field.

Best regards,


On 18/08/21 09:53, Eelke Spaak wrote:
> Dear Eric and all,
> Following up on your point (1), I've wondered in the past about the
> one-sample randomization test (let's call it that to avoid the name
> "permutation"). Would it not be fair to say that the null hypothesis
> here is that the expected value of our data equals zero?
> Cheers,
> Eelke
> On 19 August 2018 at 23:57, Maris, E.G.G. (Eric)<e.maris at donders.ru.nl>  wrote:
>> 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.
>> best,
>> Eric Maris
>>    1. Re: One-sample cluster based permutation t-test ERP data
>>       (Stephen Politzer-Ahles)
>> From: Stephen Politzer-Ahles<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>
>> 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
>> http://www.mypolyuweb.hk/~sjpolit/
>>> ----------------------------------------------------------------------
>>> Message: 1
>>> Date: Fri, 17 Aug 2018 16:01:29 +0000
>>> From: Alex Sel<alex.sel at psy.ox.ac.uk>
>>> To:"fieldtrip at science.ru.nl"  <fieldtrip at science.ru.nl>
>>> Subject: [FieldTrip] One-sample cluster based permutation t-test ERP
>>>          data
>>> Message-ID:<47637187-A1A5-4040-8917-F04C501E65CF at psy.ox.ac.uk>
>>> Content-Type: text/plain; charset="utf-8"
>>> 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
>>> OX1 3SR
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>> https://doi.org/10.1371/journal.pcbi.1002202
>> 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
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Tzvetomir Tzvetanov, PhD

Associate Professor/Senior Researcher
School of Computer & Information,
Hefei University of Technology
Hefei, Anhui 230009, P.R.China
Email:tzvetan at hfut.edu.cn

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