[FieldTrip] Cluster-based permutation tests on pre-defined time windows
tzvetan.popov at uni-konstanz.de
Fri Feb 18 13:14:37 CET 2022
The H0 you are testing is - the data from the N-conditions is exchangeable. Not particular parameters of the data such as latency windows etc. The idea here is that if part of the data is not exchangeable then this also applies to the entire data, hence you reject H0 in favour of H1. Independent of where in time the cluster is. You confirm a significant effect, not a significant cluster. The cluster is only the basis of the former conclusion. The temporal boundaries are typically defined by the task, e.g. length of epochs, baseline window etc. Subsequently, one tests to what extent (during the actually task) the data from the two conditions is exchangeable. What is typically observed is that the longer your data the smaller the likelihood to find an effect (e.g. due to noise and/or task independent ongoing brain activity), unless it is exceptionally obvious. In which case you probably won’t need an inferential test.
You do seem to have an idea about specific latencies. If this notion stems from independent source, not your descriptive exploration and intuition about where the effect is, you can still reduce the epoch and test specifically for this latencies. In your case you would also need to bonferroni correct the p-value as you would do three tests for N1, P1 and P3.
The issue here is though- there is rarely an independent source that can justify this approach. Even if you come up with literature X that did Y, it is not necessary the case that Y would also apply in your case. So you can still try the above but you have to be very explicit in the justification, particularly why it applies to your case.
> On 18 Feb 2022, at 12:24, Stefanie Elisabeth Sturm via fieldtrip <fieldtrip at science.ru.nl> wrote:
> Dear list members,
> I have a question regarding cluster-based permutation tests using ft_timelockanalysis (monte-carlo). I am comparing two conditions of time-locked EEG data across 25 subjects and I expected to find differences in certain pre-defined latency ranges, like the N1, P2 and P3 component ranges. When I do a cluster-based tests across the entire epoch, which in my case is defined from -100 to 500 ms relative to stimulus onset, I don’t get any significant clusters. When I reduce the size of the latency range, for example from 0 to 200 ms, I get a significant cluster (which is what I expected based on visual inspection of the ERPs). Now I am unsure what is the right approach. Can you give me any advice?
> Thank you!
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