189 citations · 888 across the 107 of their papers we have counts for
12 papers · 1 filter
KenCoh: A Ranked-Based Canonical Coherence
Mara Sherlin D. Talento, Sarbojit Roy, Tania Reyes Vallejo +2
This work is inspired by the problem of characterizing a dependence measure between two cortical regions of the brain where each region contains multiple signal recordings from sev…
A Robust Topological Framework for Detecting Regime Changes in Multi-Trial Experiments with Application to Predictive Maintenance
Anass B. El-Yaagoubi, Jean-Marc Freyermuth, Hernando Ombao
We present a general and flexible framework for detecting regime changes in complex, non-stationary data across multi-trial experiments. Traditional change point detection methods…
Granger Causality for Mixed Time Series Generalized Linear Models: A Case Study on Multimodal Brain Connectivity
Luiza S. C. Piancastelli, Wagner Barreto-Souza, Norbert J. Fortin +2
This paper is motivated by studies in neuroscience experiments to understand interactions between nodes in a brain network using different types of data modalities that capture dif…
Nonlinear Causality in Time Series Networks: With Application to Motor Imagery vs Execution
Sipan Aslan, Hernando Ombao
Causal interactions in time series networks can be dynamic and nonlinear, making it difficult to identify them using conventional linear causality estimations. We propose a novel a…
Correlation-Adjusted Simultaneous Testing for Ultra High-dimensional Grouped Data
Iris Ivy Gauran, Patrick Wincy Reyes, Erniel Barrios +1
Epigenetics plays a crucial role in understanding the underlying molecular processes of several types of cancer as well as the determination of innovative therapeutic tools. To inv…
Classification of High-dimensional Time Series in Spectral Domain using Explainable Features
Sarbojit Roy, Malik Shahid Sultan, Hernando Ombao
Interpretable classification of time series presents significant challenges in high dimensions. Traditional feature selection methods in the frequency domain often assume sparsity…
Predictive Performance Test based on the Exhaustive Nested Cross-Validation for High-dimensional data
Iris Ivy Gauran, Hernando Ombao, Zhaoxia Yu
It is crucial to assess the predictive performance of a model to establish its practicality and relevance in real-world scenarios, particularly for high-dimensional data analysis.…
Topological Analysis of Seizure-Induced Changes in Brain Hierarchy Through Effective Connectivity
Anass B. El-Yaagoubi, Moo K. Chung, Hernando Ombao
Traditional Topological Data Analysis (TDA) methods, such as Persistent Homology (PH), rely on distance measures (e.g., cross-correlation, partial correlation, coherence, and parti…
Granger Causality in High-Dimensional Networks of Time Series
Sipan Aslan, Hernando Ombao
A novel approach is developed for discovering directed connectivity between specified pairs of nodes in a high-dimensional network (HDN) of brain signals. To accurately identify ca…
Statistics of Extremes for Neuroscience
Paolo V. Redondo, Matheus B. Guerrero, Raphaël Huser +1
This chapter illustrates how tools from univariate and multivariate statistics of extremes can complement classical methods used to study brain signals and enhance the understandin…
Dynamic MRI reconstruction using low-rank plus sparse decomposition with smoothness regularization
Chee-Ming Ting, Fuad Noman, Raphaël C. -W. Phan +1
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…
Spectral Topological Data Analysis of Brain Signals
Anass El-Yaagoubi, Shuhao Jiao, Moo K. Chung +1
Topological analyses of brain functional connectivity usually reduce each pair of channels to a single scalar dependence, typically the Pearson correlation, and so cannot resolve t…