we used a cluster-based permutation

we used a cluster-based permutation approach50 that is ideally suited for evaluating the reliability of neural patterns at multiple neighboring data points, as in our case along the dimensions of time and (for the spectral analysis) frequency. This approach effectively circumvents the multiple-comparisons problem by evaluating clusters in the observed group-level data against a single permutation distribution of the largest clusters that are found after random permutations (or sign-flipping) of the trial-average data at the participant-level. We used 10,000 permutations and used Fieldtrip’s default cluster-settings (grouping adjacent same-signed data points that were significant in a mass univariate t-test at a two-sided alpha level of 0.05, and defining cluster-size as the sum of all t values in a cluster). The P value for each cluster in the non-permuted data is calculated as the proportion of permutations for which the size of the largest cluster is larger than the size of the considered cluster in the non-permuted data. When zero permutations yield a larger cluster (as was the case for all our analyses), this Monte Carlo P value is thus smaller than 1/N permutations (in our case P