Cluster permutation analysis for EEG series based on non-parametric Wilcoxon–Mann–Whitney statistical tests

Cluster-based permutation tests are widely used in neuroscience studies for the analysis of high-dimensional electroencephalography (EEG) and event-related potential (ERP) data as it may address multiple comparison problem without reducing statistical power. However, classical cluster-based relies on parametric t-tests, whose assumptions not be verified case non-normality distribution alternative options considered. To overcome this limitation, here we present a new software cluster EEG series based non-parametric Wilcoxon–Mann–Whitney tests. We tested both t-test Wilcoxon implementations two independent datasets ERPs spectral data: while t-test-based Wilcoxon-based analyses showed similar results ERP data, implementation was able to find clustered effects data. encourage use statistics provide publicly available computation.