Accounting for time dependence in large-scale multiple testing of event-related potential data

Ching-Fan Sheu (1) , Emeline Perthame (2, 3) , Yuh-Shiow Lee (4) , David Causeur (2, 3)

1 NCKU - National Cheng Kung University
2 IRMAR - Institut de Recherche Mathématique de Rennes
3 LMA2 - Laboratoire de Mathématiques Appliquées Agrocampus
4 Research Institute of Industrial Science and Technology

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.

Mots clés: multiple testing; high-dimensional data; ERP data; dependence

Domaines: Statistiques [math.ST]

Ching-Fan Sheu, Emeline Perthame, Yuh-Shiow Lee, David Causeur. Accounting for time dependence in large-scale multiple testing of event-related potential data. Annals of Applied Statistics, 2016, 10 (1), pp.219-245. ⟨10.1214/15-AOAS888⟩. ⟨hal-01338701⟩