Spectral methods for functional brain imaging

Abstract

Dynamic functional imaging experiments typically generate large, multivariate data sets that contain considerable spatial and temporal complexity. The goal of this introduction is to present signal-processing techniques that allow the underlying spatiotemporal structure to be readily distilled and that also enable signal versus noise contributions to be separated.