M/EEG data naturally contains spatial correlations, whether the signals are represented in sensor space or source space, as temporal patterns or time–frequency representations. Moreover, due to filtering and even the characteristics of the signals themselves, there are typically strong temporal correlations as well. Mass univariate methods provide statistical contrasts at each “location” across all dimensions, e.g., at each spatio-temporal point in a cortical temporal pattern, independently. However, due to the highly correlated nature of the data, the resulting Bonferroni or false discovery rate corrections (Benjamini and Hochberg, 1995) are generally overly conservative. Moreover, making inferences over individual spatio-temporal (or other dimensional) points is typically not of principal interest. Instead, studies typically seek to identify contiguous regions within some particular dimensionality, be it spatio-temporal or time–frequency, during which activation is greater in one condition compared to a baseline or another condition. This leads to the use of cluster-based statistics, which seek such contiguous regions of significant activation (Maris and Oostenveld, 2007).
