Modeling time-varying brain networks with a self-tuning optimized Kalman filter.

ABSTRACT: Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise components, however, modeling dynamic brain networks has remained one of the major challenges in contemporary neuroscience. Here, we present a new algorithm based on an innovative formulation of the Kalman filter that is optimized for tracking rapidly evolving patterns of directed functional connectivity under unknown noise conditions. The Self-Tuning Optimized Kalman filter (STOK) is a novel adaptive filter that embeds a self-tuning memory decay and a recursive regularization to guarantee high network tracking accuracy, temporal precisio

SUBMITTER: Pascucci D 

PROVIDER: S-EPMC7451990 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature