189 citations · 719 across the 19 of their papers we have counts for
5 papers · 1 filter
BICNet: A Bayesian Approach for Estimating Task Effects on Intrinsic Connectivity Networks in fMRI Data
Meini Tang, Chee-Ming Ting, Hernando Ombao
Intrinsic connectivity networks (ICNs) are specific dynamic functional brain networks that are consistently found under various conditions including rest and task. Studies have sho…
Modeling Brain Connectivity with Graphical Models on Frequency Domain
Xu Gao, Weining Shen, Chee-Ming Ting +3
Multichannel electroencephalograms (EEGs) have been widely used to study cortical connectivity during acquisition of motor skills. In this paper, we introduce copula Gaussian graph…
Multi-Scale Factor Analysis of High-Dimensional Brain Signals
Chee-Ming Ting, Hernando Ombao, Sh-Hussain Salleh
In this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-…
Estimating Time-Varying Effective Connectivity in High-Dimensional fMRI Data Using Regime-Switching Factor Models
Chee-Ming Ting, Hernando Ombao, S. Balqis Samdin +1
Recent studies on analyzing dynamic brain connectivity rely on sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes…
Exploratory Analysis of High Dimensional Time Series with Applications to Multichannel Electroencephalograms
Yuxiao Wang, Chee-Ming Ting, Hernando Ombao
In this paper, we address the the major hurdle of high dimensionality in EEG analysis by extracting the optimal lower dimensional representations. Using our approach, connectivity…