Signal Processing 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

Multi-Channel Analysis For Gradient Artifact Removal From Concurrent Eeg-Fmri Studies, Miguel R. Castellanos, Zhongming Liu

The Summer Undergraduate Research Fellowship (SURF) Symposium

Concurrent electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) recordings are susceptible to large amounts of noise due to the static and dynamic magnetic fields present inside the MR scanner. EEG-fMRI studies are conducted to provide better spatial and temporal resolution for each recording, respectively, but the artifacts found in the EEG render the data impossible to interpret. Past studies have focused on signal post-processing techniques which are able to effectively remove noise upon the completion of a study, but there are no techniques able to process the data in real-time without extensive calibration. This research addresses this issue by …

Design, Characterization And Application Of A Multiple Input Stethoscope Apparatus, Spencer Geng Wong

Master's Theses

For this project, the design, implementation, characterization, calibration and possible applications of a multiple transducer stethoscope apparatus were investigated. The multi-transducer sensor array design consists of five standard stethoscope diaphragms mounted to a rigid frame for a-priori knowledge of their relative spatial locations in the x-y plane, with compliant z-direction positioning to ensure good contact and pressure against the subject’s skin for reliable acoustic coupling. When this apparatus is properly placed on the body, it can digitally capture the same important body sounds investigated with standard acoustic stethoscopes; especially heart sounds. Acoustic signal inputs from each diaphragm are converted to …

Human Metaphase Chromosome Analysis Using Image Processing, Akila M.S Subasinghe Arachchige

Electronic Thesis and Dissertation Repository

Development of an effective human metaphase chromosome analysis algorithm can optimize expert time usage by increasing the efficiency of many clinical diagnosis processes. Although many methods exist in the literature, they are only applicable for limited morphological variations and are specific to the staining method used during cell preparation. They are also highly influenced by irregular chromosome boundaries as well as the presence of artifacts such as premature sister chromatid separation.

Therefore an algorithm is proposed in this research which can operate with any morphological variation of the chromosome across images from multiple staining methods. The proposed algorithm is capable …

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 …

Clear Circuit Contact Lens, Paul Hecker Ii, Phillip Azar, Alexander Do, Benny Ng, Errol Leon

Electrical Engineering

The clear active contact lens project aims to address safety and hazard awareness with an unexplored field of eye wear technology. With advancements in nanotechnology and the advent of circuits on contact lens, this project is one of the first research and development into this new field, following University of Washington and Google. The team focuses on the safety and biocompatibility of the contact lens for a comfortable ease of use. The designs push the limits of thin film printed technology with its pursuit of fine designs of 250μm antennas. The project streamlines the manufacturing process for a combination substrate …

A Method For Non-Invasive, Automated Behavior Classification In Mice, Using Piezoelectric Pressure Sensors, Steven R. Gooch

Theses and Dissertations--Electrical and Computer Engineering

While all mammals sleep, the functions and implications of sleep are not well understood, and are a strong area of investigation in the research community. Mice are utilized in many sleep studies, with electroencephalography (EEG) signals widely used for data acquisition and analysis. However, since EEG electrodes must be surgically implanted in the mice, the method is high cost and time intensive. This work presents an extension of a previously researched high throughput, low cost, non-invasive method for mouse behavior detection and classification. A novel hierarchical classifier is presented that classifies behavior states including NREM and REM sleep, as well …

Quantized Nonnegative Matrix Factorization, Ruairí De Fréin

Conference papers

Even though Nonnegative Matrix Factorization (NMF) in its original form performs rank reduction and signal compaction implicitly, it does not explicitly consider storage or transmission constraints. We propose a Frobenius-norm Quantized Nonnegative Matrix Factorization algorithm that is 1) almost as precise as traditional NMF for decomposition ranks of interest (with in 1-4dB), 2) admits to practical encoding techniques by learning a factorization which is simpler than NMF’s (by a factor of 20-70) and 3) exhibits a complexity which is comparable with state-of-the-art NMF methods. These properties are achieved by considering the quantization residual via an outer quantization optimization step, in …