6 citations · 15 across the 8 of their papers we have counts for
5 papers · 1 filter
Brain Waves Analysis Via a Non-parametric Bayesian Mixture of Autoregressive Kernels
Guillermo Granados-Garcia, Mark Fiecas, Babak Shahbaba +2
The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify predefined frequency bands that have the most substantia…
Smooth Online Parameter Estimation for time varying VAR models with application to rat's LFP data
Anass El Yaagoubi Bourakna, Marco Pinto, Norbert Fortin +1
Multivariate time series data appear often as realizations of non-stationary processes where the covariance matrix or spectral matrix smoothly evolve over time. Most of the current…
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
Lingge Li, Dustin Pluta, Babak Shahbaba +3
Dynamic functional connectivity, as measured by the time-varying covariance of neurological signals, is believed to play an important role in many aspects of cognition. While many…
Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices
Shiwei Lan, Andrew Holbrook, Gabriel A. Elias +3
Modeling correlation (and covariance) matrices can be challenging due to the positive-definiteness constraint and potential high-dimensionality. Our approach is to decompose the co…
Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials
Xu Gao, Weining Shen, Babak Shahbaba +2
We propose an evolutionary state space model (E-SSM) for analyzing high dimensional brain signals whose statistical properties evolve over the course of a non-spatial memory experi…