[FieldTrip] Cluster-based permutation tests for between-subject design

Eelke Spaak eelke.spaak at donders.ru.nl
Wed Sep 24 07:59:31 CEST 2014

Hello Dylan,

You can analyse a between-subjects design exactly as you would a
between-trials design (at least as far as the statistics step is
concerned), in both cases the two conditions correspond to two groups
of observations, and not to the same group of observations measured in
two separate conditions (which would be a within-UO design). In
FieldTrip, you would typically compute averages per subject, then use
an "indepsamplesT" (or indepsamplesF with >2 conditions) statistic
(not depsamples). indepsamplesT only requires one row in the design
matrix, indicating the condition.

Note that if you have e.g. timelock structures in two (or more) cell
arrays, corresponding to the conditions, you can input them into the
statistics function as follows:

stat = ft_timelockstatistics(cfg, tlCondA{:}, tlCondB{:});

without having to call ft_timelockgrandaverage. In fact, the above is
the preferred way to do statistics now. (The same holds for

Hope that helps,

On 24 September 2014 02:32, Dylan DeLosAngeles
<dylan.delosangeles at gmail.com> wrote:
> Hello,
> So far, the tutorial on "Cluster-based permutation tests on time-frequency
> data" has been very helpful.
> Out of the four combinations from the two UO-types (subjects and trials) and
> the two experimental designs (between- and within-UO), the tutorial covers
> statistics on data in two conditions in a between-trials, in a within-trials
> and in a within-subjects design. However, I am wondering if there is any
> information about the fourth type of experiment design: between-subjects.
> I have data for 2 groups with 12 subjects in each group. Both groups are
> measured during 11 conditions.
> Can I approach this in a similar fashion to within-subjects design (multiple
> subjects in multiple experimental conditions), such that my design is
> multiple groups in multiple experimental conditions. Is it a case of first
> averaging over all trials belonging to each of the experimental conditions
> for each subject (as instructed in tutorial), and then averaging over all
> subjects in each group?
> Configuration code for setting up the design currently looks like this;
> grp = 2;
> subj = 11;
> design = zeros(2, subj*grp);
> for i = 1:grp
>     design(1,i:2:end) = i;
> end
> idx = 1;
> for i = 1:subj
>     design(2,idx:idx+1) = i;
>     idx = idx+2;
> end
> Is there anything else I need to take into consideration when doing these
> statistics?
> Thank you,
> Dr Dylan DeLosAngeles
> Research Fellow
> Brain Signal Laboratory
> Flinders University
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