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BACKGROUND: In recent years, analyses of event related potentials/fields have moved from the selection of a few components and peaks to a mass-univariate approach in which the whole data space is analyzed. Such extensive testing increases the number of false positives and correction for multiple comparisons is needed. METHOD: Here we review all cluster-based correction for multiple comparison methods (cluster-height, cluster-size, cluster-mass, and threshold free cluster enhancement - TFCE), in conjunction with two computational approaches (permutation and bootstrap). RESULTS: Data driven Monte-Carlo simulations comparing two conditions within subjects (two sample Student's t-test) showed that, on average, all cluster-based methods using permutation or bootstrap alike control well the family-wise error rate (FWER), with a few caveats. CONCLUSIONS: (i) A minimum of 800 iterations are necessary to obtain stable results; (ii) below 50 trials, bootstrap methods are too conservative; (iii) for low critical family-wise error rates (e.g. p=1%), permutations can be too liberal; (iv) TFCE controls best the type 1 error rate with an attenuated extent parameter (i.e. power<1).

Original publication

DOI

10.1016/j.jneumeth.2014.08.003

Type

Journal article

Journal

J Neurosci Methods

Publication Date

30/07/2015

Volume

250

Pages

85 - 93

Keywords

Cluster-based statistics, ERP, Family-wise error rate, Monte-Carlo simulations, Multiple comparison correction, Threshold free cluster enhancement, Brain, Cluster Analysis, Computer Simulation, Datasets as Topic, Electroencephalography, Evoked Potentials, Humans, Monte Carlo Method, Signal Processing, Computer-Assisted, Software