6 citations · 15 across the 8 of their papers we have counts for
8 papers
A Model-Agnostic Graph Neural Network for Integrating Local and Global Information
Wenzhuo Zhou, Annie Qu, Keiland W. Cooper +2
Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limi…
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…
Bayesian Neural Decoding Using A Diversity-Encouraging Latent Representation Learning Method
Tian Chen, Lingge Li, Gabriel Elias +2
It is well established that temporal organization is critical to memory, and that the ability to temporally organize information is fundamental to many perceptual, cognitive, and m…
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…
Modeling high dimensional multichannel brain signals
Lechuan Hu, Norbert Fortin, Hernando Ombao
Our goal is to model and measure functional and effective (directional) connectivity in multichannel brain physiological signals (e.g., electroencephalograms, local field potential…
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…