Parametric estimation of spectrum driven by an exogenous signal

• Published in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 4301 - 4305
• Main Authors: Dupre la Tour, Tom, Grenier, Yves, Gramfort, Alexandre
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-03-2017
• Subjects: Biological system modeling; Econometrics; electrophysiology; Estimation; Lattices; non-linear auto-regressive models; non-stationary; phase-amplitude coupling; spectrum estimation; Stability analysis; Technological innovation
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP.2017.7952968

In this paper, we introduce new parametric generative driven auto-regressive (DAR) models. DAR models provide a nonlinear and non-stationary spectral estimation of a signal, conditionally to another exogenous signal. We detail how inference can be done efficiently while guaranteeing model stability. We show how model comparison and hyper-parameter selection can be done using likelihood estimates. We also point out the limits of DAR models when the exogenous signal contains too high frequencies. Finally, we illustrate how DAR models can be applied on neuro-physiologic signals to characterize phase-amplitude coupling.