BayesRPAGP

Bayesian Random Phase-Amplitude Gaussian Process

BayesRPAGP in an R software for Bayesian inference of trial-level amplitude, latency, and ERP waveforms. Motivated by the need for rigorous, flexible, and interpretable methods for trial-level analysis of ERP data, we developed the Random Phase-Amplitude Gaussian Process (RPAGP) modeling framework, which assumes that individual trials are generated as a common structural signal transformed by a trial-specific amplitude and phase shift plus ongoing brain activity. In the proposed RPAGP framework, the unknown signal is modeled via a Gaussian Process (GP) prior and an autoregressive process is assumed for the ongoing brain activity. We set priors on the trial-specific model parameters and design an efficient algorithm for posterior inference. Further details are provided in Pluta, D., Hadj-Amar, B., Li., M., Zhao, Y., Versace, F., Vannucci., M, (2024) "Improved Data Quality and Statistical Power of Trial-Level Event-Related Potentials with Bayesian Random-Shift Gaussian Processes", published in Nature Portfolio, Scientific Reports.

The software was developed by B. Hadj-Amar and D. Pluta. For any comments, please contact B.Hadj-Amar at bh44@rice.edu.

Example

We provide a snapshot of tutorial.Rmd, which contains a tutorial for using our software in R

• Run RAPGP sampler

  results <- fit_rpagp(y = dat$y, n_iter = n_iter,
                         theta0 = theta0, hyperparam = hyperparam,
                         pinned_point = pinned_point,
                         pinned_value = pinned_value)