Journal Article

Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise

Miran, Sina, Babadi, Behtash, Presacco, Alessandro, Marcus, Steven I., Fu, Michael C., Simon, Jonathan Z.

2020

Overview

Estimating the latent dynamics underlying biological processes is a central problem in computational biology. State-space models with Gaussian statistics are widely used for estimation of such latent dynamics and have been successfully utilized in the analysis of biological data. Gaussian statistics, however, fail to capture several key features of the dynamics of biological processes (e.g., brain dynamics) such as abrupt state changes and exogenous processes that affect the states in a structured fashion. Although Gaussian mixture process noise models have been considered as an alternative to capture such effects, data-driven inference of their parameters is not well-established in the literature. The objective of this paper is to develop efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and to utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting. We develop an algorithm based on Expectation-Maximization to estimate the process noise parameters from state-space observations. We apply our algorithm to simulated and experimentally-recorded MEG data from auditory experiments in the cocktail party paradigm to estimate the underlying dynamic Temporal Response Functions (TRFs). Our simulation results show that the richer representation of the process noise as a Gaussian mixture significantly improves state estimation and capturing the heterogeneity of the TRF dynamics. Application to MEG data reveals improvements over existing TRF estimation techniques, and provides a reliable alternative to current approaches for probing neural dynamics in a cocktail party scenario, as well as attention decoding in emerging applications such as smart hearing aids. Our proposed methodology provides a framework for efficient inference of Gaussian mixture process noise models, with application to a wide range of biological data with underlying heterogeneous and latent dynamics.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Algorithms / Auditory Pathways - physiology / Biological activity / Biological analysis / Biology and Life Sciences / Central auditory processing / Computational neuroscience / Computer engineering / Computer simulation / Data analysis / Dynamics / Engineering and Technology / Evolution / Funding / Gaussian process / Gaussian processes / Hearing aids / Heterogeneity / Humans / Inference / Information processing / Kalman filters / Magnetoencephalography / Magnetoencephalography - methods / Medical imaging / Models, Neurological / Noise / Noise (Sound) / Parameter estimation / Physical Sciences / Physiological aspects / Process parameters / Research and Analysis Methods / Response functions / Signal processing / Social Sciences / State estimation / State space models / Statistical analysis / Supervision / Time series