Surrogate data methods based on a shuffling of the trials for synchrony detection: the centering issue

Abstract : We investigate several distribution free dependence detection procedures, mainly based on bootstrap principles and their approximation properties. Thanks to this study, we introduce a new distribution free Unitary Events (UE) method, named Permutation UE, which consists in a multiple testing procedure based on permutation and delayed coincidence count. Each involved single test of this procedure achieves the prescribed level, so that the corresponding multiple testing procedure controls the False Discovery Rate (FDR), and this with as few assumptions as possible on the underneath distribution. Some simulations show that this method outperforms the trial-shuffling and the MTGAUE method in terms of single levels and FDR, for a comparable amount of false negatives. Application on real data is also provided.
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Submitted on : Tuesday, July 12, 2016 - 10:33:24 AM
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Mélisande Albert, Yann Bouret, Magalie Fromont, Patricia Reynaud-Bouret. Surrogate data methods based on a shuffling of the trials for synchrony detection: the centering issue. Neural Computation, Massachusetts Institute of Technology Press (MIT Press), 2016, 28 (1), pp.2352-2392. ⟨10.1162/NECO_a_00839⟩. ⟨hal-01154918v2⟩

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