For every sample (a (channel,time)-

For every sample (a (channel,time)-pair or a (channel,frequency,time)-triplet) the experimental conditions are compared by means of a t-value or some other number that quantifies the effect at this sample. It must be noted that this t-value is not the cluster-based test statistic for which we will calculate the significance probability; it is just an ingredient in the calculation of this cluster-based test statistic. Quantifying the effect at the sample level is possible by means of different measures. These measures are specified in cfg.statistic, which can have the values ‘ft_statfun_indepsamplesT’, ‘ft_statfun_depsamplesT’, ‘ft_statfun_actvsblT’, and many others. We will return to this in the following.All samples are selected whose t-value is larger than some threshold as specified in cfg.clusteralpha. It must be noted that the value of cfg.clusteralpha does not affect the false alarm rate of the statistical test; it only sets a threshold for considering a sample as a candidate member of some cluster of samples. If cfg.clusteralpha is equal to 0.05, the t-values are thresholded at the 95-th quantile for a one-sided t-test, and at the 2.5-th and the 97.5-th quantiles for a two-sided t-test.Selected samples are clustered in connected sets on the basis of temporal, spatial and spectral adjacency.Cluster-level statistics are calculated by taking the sum of the t-values within every cluster.The maximum of the cluster-level statistics is taken. This step and the previous one (step 4) are controlled by cfg.clusterstatistic, which can have the values ‘maxsum’, ‘maxsize’, or ‘wcm’. In this tutorial, we will only use ‘maxsum’, which is the default value. The result from step 5 is the test statistic by means of which we evaluate the effect of the experimental conditions.