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March 2016 Accounting for time dependence in large-scale multiple testing of event-related potential data
Ching-Fan Sheu, Émeline Perthame, Yuh-shiow Lee, David Causeur
Ann. Appl. Stat. 10(1): 219-245 (March 2016). DOI: 10.1214/15-AOAS888

Abstract

Event-related potentials (ERPs) are recordings of electrical activity along the scalp time-locked to perceptual, motor and cognitive events. Because ERP signals are often rare and weak, relative to the large between-subject variability, establishing significant associations between ERPs and behavioral (or experimental) variables of interest poses major challenges for statistical analysis.

Noting that ERP time dependence exhibits a block pattern suggesting strong local and long-range autocorrelation components, we propose a flexible factor modeling of dependence. An adaptive factor adjustment procedure is derived from a joint estimation of the signal and noise processes, given a prior knowledge of the noise-alone intervals. A simulation study is presented using known signals embedded in a real dependence structure extracted from authentic ERP measurements. The proposed procedure performs well compared with existing multiple testing procedures and is more powerful at discovering interesting ERP features.

Citation

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Ching-Fan Sheu. Émeline Perthame. Yuh-shiow Lee. David Causeur. "Accounting for time dependence in large-scale multiple testing of event-related potential data." Ann. Appl. Stat. 10 (1) 219 - 245, March 2016. https://doi.org/10.1214/15-AOAS888

Information

Received: 1 July 2014; Revised: 1 October 2015; Published: March 2016
First available in Project Euclid: 25 March 2016

zbMATH: 06586143
MathSciNet: MR3480494
Digital Object Identifier: 10.1214/15-AOAS888

Keywords: Dependence , ERP data , High-dimensional data , multiple testing

Rights: Copyright © 2016 Institute of Mathematical Statistics

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Vol.10 • No. 1 • March 2016
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