title: Granger causality with signal-dependent noise.
abstract: It is generally believed that the noise variance in in vivo neuronal data exhibits time-varying volatility, particularly signal-dependent noise. Despite a widely used and powerful tool to detect causal influences in various data sources, Granger causality has not been well tailored for time-varying volatility models. In this technical note, a unified treatment of the causal influences in both mean and variance is naturally proposed on models with signal-dependent noise in both time and frequency domains. The approach is first systematically validated on toy models, and then applied to the physiological data collected from Parkinson patients, where a clear advantage over the classical Granger causality is demonstrated.
authors.affiliation: Department of Mathematics, National University of Defense Technology, Hunan, PR China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Luo
authors.forename: Tian
authors.initials: T
authors.lastname: Ge
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D004576:Electromyography; D006801:Humans; D008959:Models, Neurological; D010300:Parkinson Disease / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted