Search Results for author:
Found 16 papers, 4 papers with code
Separating Stimulus-Induced and Background Components of Dynamic Functional Connectivity in Naturalistic fMRI
We propose a novel, data-driven approach based on low-rank plus sparse (L+S) decomposition to isolate stimulus-driven dynamic changes in brain functional connectivity (FC) from the background noise, by exploiting shared network structure among subjects receiving the same naturalistic stimuli.
Granger Causality using Neural Networks
It helps in answering the question whether one time series is helpful in forecasting.
Dynamic Topological Data Analysis of Functional Human Brain Networks
We propose a novel dynamic-TDA framework that builds persistent homology over a time series of brain networks.
Intrinsic data depth for Hermitian positive definite matrices
Nondegenerate covariance, correlation and spectral density matrices are necessarily symmetric or Hermitian and positive definite.
Methodology 62G30, 62G15, 62G35, 62M15
Short-segment heart sound classification using an ensemble of deep convolutional neural networks
This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats.
Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network
We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ).
A Markov-Switching Model Approach to Heart Sound Segmentation and Classification
Methods: We propose an approach based on Markov switching autoregressive model (MSAR) to segmenting the HS into four fundamental components each with distinct second-order structure.
Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach
We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time.
Smooth Online Parameter Estimation for time varying VAR models with application to rat's LFP data
For these reasons we propose a new smooth online parameter estimation approach (SOPE) that has the ability to control for the smoothness of the estimates with a reasonable computational complexity.
Time Series Analysis
Methodology
Computation
Other Statistics
Conex-Connect: Learning Patterns in Extremal Brain Connectivity From Multi-Channel EEG Data
Our proposed method, the conditional extremal dependence for brain connectivity (Conex-Connect), is a pioneering approach that links the association between extreme values of higher oscillations at a reference channel with the other brain network channels.
Lattice Paths for Persistent Diagrams
We then develop a new exact statistical inference procedure for lattice paths via combinatorial enumerations.
Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification
Our new framework demonstrates feasibility of learning graph embeddings on brain networks to provide discriminative information for diagnosis of brain disorders.
Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation
Despite its remarkable success for Euclidean-valued data generation, use of standard generative adversarial networks (GANs) to generate manifold-valued FC data neglects its inherent SPD structure and hence the inter-relatedness of edges in real FC.
Stylized Projected GAN: A Novel Architecture for Fast and Realistic Image Generation
Generative Adversarial Networks are used for generating the data using a generator and a discriminator, GANs usually produce high-quality images, but training GANs in an adversarial setting is a difficult task.
Spectral Topological Data Analysis of Brain Signals
Our approach, the spectral TDA (STDA), has the ability to capture more nuanced and detailed information about the underlying brain networks.
Dynamic MRI reconstruction using low-rank plus sparse decomposition with smoothness regularization
The low-rank plus sparse (L+S) decomposition model has enabled better reconstruction of dynamic magnetic resonance imaging (dMRI) with separation into background (L) and dynamic (S) component.
