we used a cluster-based permutation

we used a cluster-based permutation approach50 that is ideallysuited for evaluating the reliability of neural patterns at multiple neighboring datapoints, as in our case along the dimensions of time and (for the spectral analysis)frequency. This approach effectively circumvents the multiple-comparisonsproblem by evaluating clusters in the observed group-level data against a singlepermutation distribution of the largest clusters that are found after randompermutations (or sign-flipping) of the trial-average data at the participant-level. Weused 10,000 permutations and used Fieldtrip’s default cluster-settings (groupingadjacent same-signed data points that were significant in a mass univariate t-test ata two-sided alpha level of 0.05, and defining cluster-size as the sum of all t valuesin a cluster). The P value for each cluster in the non-permuted data is calculatedas the proportion of permutations for which the size of the largest cluster is largerthan the size of the considered cluster in the non-permuted data. When zeropermutations yield a larger cluster (as was the case for all our analyses), this MonteCarlo P value is thus smaller than 1/N permutations (in our case P