189 citations · 888 across the 107 of their papers we have counts for
13 papers · 1 filter
Canonical Tail Dependence for Soft Extremal Clustering of Multichannel Brain Signals
Mara Sherlin Talento, Jordan Richards, Raphael Huser +1
We develop a novel characterization of extremal dependence between two cortical regions of the brain when its signals display extremely large amplitudes. We show that connectivity…
Nonlinear Coherence for Vector Time Series: Defining Region-to-Region Functional Brain Connectivity
Paolo Victor Redondo, Raphaël Huser, Hernando Ombao
Alterations in functional brain connectivity characterize neurodegenerative disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD). As a non-invasive and cost…
Robust fuzzy clustering for high-dimensional multivariate time series with outlier detection
Ziling Ma, Ángel López-Oriona, Hernando Ombao +1
Fuzzy clustering provides a natural framework for modeling partial memberships, particularly important in multivariate time series (MTS) where state boundaries are often ambiguous.…
T2I-Diff: fMRI Signal Generation via Time-Frequency Image Transform and Classifier-Free Denoising Diffusion Models
Hwa Hui Tew, Junn Yong Loo, Yee-Fan Tan +5
Functional Magnetic Resonance Imaging (fMRI) is an advanced neuroimaging method that enables in-depth analysis of brain activity by measuring dynamic changes in the blood oxygenati…
EEG-MSAF: An Interpretable Microstate Framework uncovers Default-Mode Decoherence in Early Neurodegeneration
Mohammad Mehedi Hasan, Pedro G. Lind, Hernando Ombao +2
Dementia (DEM) is a growing global health challenge, underscoring the need for early and accurate diagnosis. Electroencephalography (EEG) provides a non-invasive window into brain…
Robust Spectral Fuzzy Clustering of Multivariate Time Series with Applications to Electroencephalogram
Ziling Ma, Mara Sherlin Talento, Ying Sun +1
Clustering multivariate time series (MTS) is challenging due to non-stationary cross-dependencies, noise contamination, and gradual or overlapping state boundaries. We introduce a…
Emergent Granger Causality in Neural Networks: Can Prediction Alone Reveal Structure?
Malik Shahid Sultan, Hernando Ombao, Maurizio Filippone
Granger Causality (GC) offers an elegant statistical framework to study the association between multivariate time series data. Vector autoregressive models (VAR) are simple and eas…
Filtration-Based Learning of Multiscale Shared Structures for Multiple Functional Predictors
Shuhao Jiao, Hernando Ombao, Ian W. McKeague
It is crucial to learn the shared structures among functional predictors, as these structures characterize how predictor components exert common effects and, more generally, how pr…
FCPCA: Fuzzy clustering of high-dimensional time series based on common principal component analysis
Ziling Ma, Ángel López-Oriona, Hernando Ombao +1
Clustering multivariate time series data is a crucial task in many domains, as it enables the identification of meaningful patterns and groups in time-evolving data. Traditional ap…
Wavelet Canonical Coherence for Nonstationary Signals
Haibo Wu, Marina I. Knight, Keiland W. Cooper +2
Understanding the evolving dependence between two clusters of multivariate signals is fundamental in neuroscience and other domains where sub-networks in a system interact dynamica…
From Density to Void: Why Brain Networks Fail to Reveal Complex Higher-Order Structures
Moo K. Chung, Anass B. El-Yaagoubi, Anqi Qiu +1
In brain network analysis using resting-state fMRI, there is growing interest in modeling higher-order interactions beyond simple pairwise connectivity via persistent homology. Des…
Spectral Extremal Connectivity of Two-State Seizure Brain Waves
Mara Sherlin D. Talento, Jordan Richards, Marco Pinto-Orellana +2
Coherence analysis plays a vital role in the study of functional brain connectivity. However, coherence captures only linear spectral associations, and thus can produce misleading…
SFC-GAN: A Generative Adversarial Network for Brain Functional and Structural Connectome Translation
Yee-Fan Tan, Jun Lin Liow, Pei-Sze Tan +4
Modern brain imaging technologies have enabled the detailed reconstruction of human brain connectomes, capturing structural connectivity (SC) from diffusion MRI and functional conn…