Bayesian models for event-related potentials time-series and electrode correlations estimation

Abstract

Recent developments on Bayesian inference and prediction for effects on event-related potentials (ERPs) stem from a wide variety of methods, including machine learning, multilevel models, and others. However, few of these approaches make use of clear estimates of the ERP voltage across time and across space (scalp). In the present article, via an iterative process, we propose Gaussian random walk (GRW) models that can estimate voltage uncertainty across time and also provide correlation matrices across electrodes. We apply these models to real ERP data from a P3b paradigm as an example. We discuss results in terms of past and current literature of both ERP estimation and electroencephalography analysis in general.