Full-Head fNIRS Data Generator Coupled with a Neural Posterior Estimation

Abstract

We generate synthetic fNIRS time series data with preserved spatiotemporal information, including systemic physiology, using Mesh-based Monte Carlo modeling. To replicate experimental data features, we iteratively calibrate the parameter space of the simulator with BayesFlow.