Engineering Commons^™

Estimating Effective Connectivity From Fmri Data Using Factor-Based Subspace Autoregressive Models, Chee-Ming Ting Phd, Abd-Krim Seghouane Phd, Sh-Hussain Salleh Phd, Alias M. Noor Phd

Chee-Ming Ting

Modeling And Estimation Of Single-Trial Event-Related Potentials Using Partially Observed Diffusion Processes, Chee-Ming Ting Phd, Sh-Hussain Salleh, Z. M. Zainuddin, Arifah Bahar

Chee-Ming Ting

This paper proposes a new modeling framework for estimating single-trial event-related potentials (ERPs). Existing studies based on state-space approach use discrete-time random-walk models. We propose to use continuous-time partially observed diffusion process which is more natural and appropriate to describe the continuous dynamics underlying ERPs, discretely observed in noise as single-trials. Moreover, the flexibility of the continuous-time model being specified and analyzed independently of observation intervals, enables a more efficient handling of irregularly or variably sampled ERPs than its discrete-time counterpart which is fixed to a particular interval. We consider the Ornstein–Uhlenbeck (OU) process for the inter-trial parameter dynamics and …

Estimation Of High-Dimensional Brain Connectivity From Fmri Data Using Factor Modeling, Chee-Ming Ting Phd, Abd-Krim Seghouane, Sh-Hussain Salleh, Alias M. Noor

Chee-Ming Ting

We consider identifying effective connectivity of brain networks from fMRI time series. The standard vector autoregressive (VAR) models fail to give reliable network estimates, typically involving very large number of nodes. This paper adopts a dimensionality reduction approach based on factor modeling, to enable effective and efficient high-dimensional VAR analysis of large network connectivity. We derive a subspace VAR (SVAR) model from the factor model (FM) in which the observations are driven by a lower dimensional subspace of common latent factors, following an autoregressive dynamics. We consider the principal components (PC) method which can produce consistent estimators for the FM, …

Estimating Dynamic Cortical Connectivity From Motor Imagery Eeg Using Kalman Smoother & Em Algorithm, S. Balqis Samdin, Chee-Ming Ting Phd, Sh-Hussain Salleh, Mahyar Hamedi, Alias Mohd Noor

Chee-Ming Ting

This paper considers identifying effective cortical connectivity from scalp EEG. Recent studies use time-varying multivariate autoregressive (TV-MAR) models to better describe the changing connectivity between cortical regions where the TV coefficients are estimated by Kalman filter (KF) within a state-space framework. We extend this approach by incorporating Kalman smoothing (KS) to improve the KF estimates, and the expectation-maximization (EM) algorithm to infer the unknown model parameters from EEG. We also consider solving the volume conduction problem by modeling the induced instantaneous correlations using a full noise covariate. Simulation results show the superiority of KS in tracking the coefficient changes. We …

Artifact Removal From Single-Trial Erps Using Non-Gaussian Stochastic Volatility Models And Particle Filter, Chee-Ming Ting Phd, Sh-Hussain Salleh, Z. M. Zainuddin, Arifah Bahar

Chee-Ming Ting

This paper considers improved modeling of artifactual noise for denoising of single-trial event-related potentials (ERPs) by state-space approach. Instead of the inadequate constant variance models used in existing studies, we propose to use stochastic volatility (SV) models to better describe the time-varying volatility in real ERP noise sources. We further propose a class of non-Gaussian SV models to capture the abrupt volatility changes typically present in impulsive noise, to improve artifact removal from ERPs. Two specifications are considered: (1) volatility driven by a heavy-tailed component and (2) transformation of volatility. Both result in volatility processes with heavy-tailed transition densities which …