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Full-Text Articles in Engineering
Functional Brain Network Classification With Compact Representation Of Sice Matrices, Jianjia Zhang, Luping Zhou, Lei Wang, Wanqing Li
Functional Brain Network Classification With Compact Representation Of Sice Matrices, Jianjia Zhang, Luping Zhou, Lei Wang, Wanqing Li
Faculty of Engineering and Information Sciences - Papers: Part A
Recently, sparse inverse covariance estimation (SICE) technique has been employed to model functional brain connectivity. The inverse covariance matrix (SICE matrix in short) estimated for each subject is used as a representation of brain connectivity to discriminate Alzheimers disease from normal controls. However, we observed that direct use of the SICE matrix does not necessarily give satisfying discrimination, due to its high dimensionality and the scarcity of training subjects. Looking into this problem, we argue that the intrinsic dimensionality of these SICE matrices shall be much lower, considering i) an SICE matrix resides on a Riemannian manifold of symmetric positive …
Eeg-Based Brain-Computer Interface For Automating Home Appliances, Abdel Ilah N. Alshbatat, Peter J. Vial, Prashan Premaratne, Le Chung Tran
Eeg-Based Brain-Computer Interface For Automating Home Appliances, Abdel Ilah N. Alshbatat, Peter J. Vial, Prashan Premaratne, Le Chung Tran
Faculty of Engineering and Information Sciences - Papers: Part A
An EEG-based brain-computer system for automating home appliances is proposed in this study. Brain-computer interface (BCI) system provides direct pathway between human brain and external computing resources or external devices. The system translates thought into action without using muscles through a number of electrodes attached to the user's scalp. The BCI technology can be used by disabled people to improve their independence and maximize their capabilities at home. In this paper, a novel BCI system was developed to control home appliances from a dedicated Graphical User Interface (GUI). The system is structured with six units: EMOTIV EPOC headset, personal computer, …
Discriminative Sparse Inverse Covariance Matrix: Application In Brain Functional Network Classification, Luping Zhou, Lei Wang, Philip O. Ogunbona
Discriminative Sparse Inverse Covariance Matrix: Application In Brain Functional Network Classification, Luping Zhou, Lei Wang, Philip O. Ogunbona
Faculty of Engineering and Information Sciences - Papers: Part A
Recent studies show that mental disorders change the functional organization of the brain, which could be investigated via various imaging techniques. Analyzing such changes is becoming critical as it could provide new biomarkers for diagnosing and monitoring the progression of the diseases. Functional connectivity analysis studies the covary activity of neuronal populations in different brain regions. The sparse inverse covariance estimation (SICE), also known as graphical LASSO, is one of the most important tools for functional connectivity analysis, which estimates the interregional partial correlations of the brain. Although being increasingly used for predicting mental disorders, SICE is basically a generative …
Hierarchical Anatomical Brain Networks For Mci Prediction: Revisiting Volumetric Measures, Luping Zhou, Yaping Wang, Yang Li, Pew-Thian Yap, Dinggang Shen, Alzheimers Disease Neuroimaging Initiative (Adni)
Hierarchical Anatomical Brain Networks For Mci Prediction: Revisiting Volumetric Measures, Luping Zhou, Yaping Wang, Yang Li, Pew-Thian Yap, Dinggang Shen, Alzheimers Disease Neuroimaging Initiative (Adni)
Faculty of Engineering and Information Sciences - Papers: Part A
Owning to its clinical accessibility, T1-weighted MRI (Magnetic Resonance Imaging) has been extensively studied in the past decades for prediction of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The volumes of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) are the most commonly used measurements, resulting in many successful applications. It has been widely observed that disease-induced structural changes may not occur at isolated spots, but in several inter-related regions. Therefore, for better characterization of brain pathology, we propose in this paper a means to extract inter-regional correlation based features from local volumetric measurements. Specifically, our approach …
Hierarchical Anatomical Brain Networks For Mci Prediction By Partial Least Square Analysis, Luping Zhou, Yaping Wang, Yang Li, Pew-Thian Yap, Dinggang Shen
Hierarchical Anatomical Brain Networks For Mci Prediction By Partial Least Square Analysis, Luping Zhou, Yaping Wang, Yang Li, Pew-Thian Yap, Dinggang Shen
Faculty of Engineering and Information Sciences - Papers: Part A
Owning to its clinical accessibility, T1-weighted MRI has been extensively studied for the prediction of mild cognitive impairment (MCI) and Alzheimer's disease (AD). The tissue volumes of GM, WM and CSF are the most commonly used measures for MCI and AD prediction. We note that disease-induced structural changes may not happen at isolated spots, but in several inter-related regions. Therefore, in this paper we propose to directly extract the inter-region connectivity based features for MCI prediction. This involves constructing a brain network for each subject, with each node representing an ROI and each edge representing regional interactions. This network is …