Hi niko808080,

A permutation test needs i) a test statistic, and ii) a resampling procedure. The test statistic is a t-statistic, because you use a t-test. The resampling procedure is based on Monte Carlo sampling to randomly exchange labels A and B between the two conditions multiple times, every time creating a new permutation sample.

I always advise to run the permutations at a subject level to ensure random effects inference. That is, if you have N subjects, for a paired t-test you will have N measurements for condition A and N measurements for condition B. (You may also have an unpaired case with N1 subjects for condition A and N2 subjects for condition B. Make sure you use the proper t-test for your data, paired or unpaired). When you randomize the labels, you exchange the entire spatiotemporal map as a whole (that is, you do not permute one electrode with another electrode, but as you wrote, you permute F3 condition 1 vs F3 condition 2). Eventually, the permutation samples allow you to convert your spatiotemporal t-statistic maps into p-value maps. Finally, you *do* need to control for multiple comparisons across the entire p-value map because the map comprises multiple statistics (across sensors and time points). FDR is the most straightforward way. You are right that a cluster-based inference does not need this last step.

You may be able to write a text similar to this in your paper, replace A and B with proper names:

“We used non-parametric statistical inference that does not make assumptions on the distributions of the data (Maris and Oostenveld, 2007; Pantazis et al., 2005). Permutation tests were performed across subjects for random effects inference. Under the null hypothesis of no PSD difference in the sensor data between the two conditions, the labels between conditions A and B for each subject could be randomly permuted and the resulting data were used to compute a permutation t-statistic spatiotemporal sensor map. Repeating this permutation procedure 1000 times, using Monte Carlo random sampling, enabled us to estimate the empirical distribution of the t-statistic at each sensor and time point, and thus convert the original data into a p-value statistical map. Last, to control for multiple comparisons across all sensors and time points, the p-values were adjusted using a false discovery rate procedure.”

Best,

Dimitrios