99 citations · 305 across the 23 of their papers we have counts for
26 papers
TwinS-GCN: Spectral conjugate for Spectral Graph Convolutional Networks
Chun Hei Michael Chan, Flavia Petruso, Dimitri Van De Ville
Graph convolutional networks propagate information by repeated local aggregation through a graph shift operator; i.e., a -layer network reaches hops neighborhood. On the one…
Graph Signal Surrogate Generation for Statistical Testing of Covariance Structure on Directed Graphs
Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De Ville
Non-parametric statistical testing is based on surrogate data generation that randomizes chosen features in the empirical data. In the graph setting, graph signal processing (GSP)…
Graph Diffusion-Advection Operator for Directed Graph Signal Processing
CHM. Chan, A. Cionca, V. Škultéty +1
Graph signal processing (GSP) provides a framework for analyzing data on irregular domains, with applications in neuroscience, finance, chemistry, and social sciences. Classical GS…
Optimal Wiener-Filter Solutions for Denoising of Graph Signals on Directed Graphs
Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De Ville
Graph signal processing has opened new avenues to the canonical denoising problem in interesting settings. Specifically, here we propose a Wiener-filter solution for graph signals…
Statistical Testing on Directed Graphs by Surrogate Data Generation
Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De Ville
In recent years, graph signal processing has emerged as a powerful framework at the intersection of signal processing and graph theory, providing tools for the analysis of signals…
Topological Signal Processing: An Application-Oriented Tutorial
Flavia Petruso, Maria Giulia Preti, Dimitri Van De Ville
Many modern datasets are large and carry complex structural relationships. Graph-based methods have traditionally been used to represent networked data, modeling individual element…
Community detection for directed networks revisited using bimodularity
Alexandre Cionca, Chun Hei Michael Chan, Dimitri Van De Ville
Community structure is a key feature omnipresent in real-world network data. Plethora of methods have been proposed to reveal subsets of densely interconnected nodes using criteria…
From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing Technical Committee
Selin Aviyente, Alejandro Frangi, Erik Meijering +6
The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical i…
Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work
Eneko Uruñuela, Thomas A. W. Bolton, Dimitri Van De Ville +1
Deconvolution of the hemodynamic response is an important step to access short timescales of brain activity recorded by functional magnetic resonance imaging (fMRI). Albeit convent…
Community-Aware Graph Signal Processing
Miljan Petrovic, Raphael Liegeois, Thomas A. W. Bolton +1
The emerging field of graph signal processing (GSP) allows to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generall…
TbCAPs: A ToolBox for Co-Activation Pattern Analysis
Thomas A. W. Bolton, Constantin Tuleasca, Gwladys Rey +7
Functional magnetic resonance imaging provides rich spatio-temporal data of human brain activity during task and rest. Many recent efforts have focussed on characterising dynamics…
Structural mediation of human brain activity revealed by white-matter interpolation of fMRI
Anjali Tarun, Hamid Behjat, David Abramian +1
Anatomy of the human brain constrains the formation of large-scale functional networks. Here, given measured brain activity in gray matter, we interpolate these functional signals…
Agito ergo sum: correlates of spatiotemporal motion characteristics during fMRI
Thomas A. W. Bolton, Daniela Zöller, César Caballero-Gaudes +3
The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-ofthe-art guidelines advise to…
Decoupling of brain function from structure reveals regional behavioral specialization in humans
Maria Giulia Preti, Dimitri Van De Ville
The brain is an assembly of neuronal populations interconnected by structural pathways. Brain activity is expressed on and constrained by this substrate. Therefore, statistical dep…
Domain-Informed Spline Interpolation
Hamid Behjat, Zafer Doğan, Dimitri Van De Ville +1
Standard interpolation techniques are implicitly based on the assumption that the signal lies on a single homogeneous domain. In contrast, many naturally occurring signals lie on a…
Augmented Slepians: Bandlimited Functions that Counterbalance Energy in Selected Intervals
Robin Demesmaeker, Maria Giulia Preti, Dimitri Van De Ville
Slepian functions provide a solution to the optimization problem of joint time-frequency localization. Here, this concept is extended by using a generalized optimization criterion…
A Graph Signal Processing View on Functional Brain Imaging
Weiyu Huang, Thomas A. W. Bolton, John D. Medaglia +3
Modern neuroimaging techniques provide us with unique views on brain structure and function; i.e., how the brain is wired, and where and when activity takes place. Data acquired us…
Interpolation in the Presence of Domain Inhomogeneity
Hamid Behjat, Zafer Doğan, Dimitri Van De Ville +1
Standard interpolation techniques are implicitly based on the assumption that the signal lies on a homogeneous domain. In this letter, the proposed interpolation method instead exp…
When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum
Dimitri Van De Ville, Robin Demesmaeker, Maria Giulia Preti
The study of complex systems benefits from graph models and their analysis. In particular, the eigendecomposition of the graph Laplacian lets emerge properties of global organizati…
Sparsity Averaging for Compressive Imaging
Rafael E. Carrillo, Jason D. McEwen, Dimitri Van De Ville +2
We discuss a novel sparsity prior for compressive imaging in the context of the theory of compressed sensing with coherent redundant dictionaries, based on the observation that nat…
Sparse regularization for fiber ODF reconstruction: from the suboptimality of and priors to
Alessandro Daducci, Dimitri Van De Ville, Jean-Philippe Thiran +1
Diffusion MRI is a well established imaging modality providing a powerful way to probe the structure of the white matter non-invasively. Despite its potential, the intrinsic long s…
Sparse image reconstruction on the sphere: implications of a new sampling theorem
J. D. McEwen, G. Puy, J. -Ph. Thiran +3
We study the impact of sampling theorems on the fidelity of sparse image reconstruction on the sphere. We discuss how a reduction in the number of samples required to represent all…
Spread spectrum magnetic resonance imaging
Gilles Puy, Jose P. Marques, Rolf Gruetter +4
We propose a novel compressed sensing technique to accelerate the magnetic resonance imaging (MRI) acquisition process. The method, coined spread spectrum MRI or simply s2MRI, cons…
A comprehensive error rate for multiple testing
Djalel Eddine Meskaldji, Dimitri Van De Ville, Jean-Philippe Thiran +1
The higher criticism of a family of tests starts with the individual uncorrected p-values of each test. It then requires a procedure for deciding whether the collection of p-values…