Sebastiano Stramaglia

发表

Nonlinear parametric model for Granger causality of time series

Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia, 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

Searching for modules of networks in the auto‐encoder frame

Daniele Marinazzo, Sebastiano Stramaglia, Leonardo Angelini, 2007 .

Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality

Luca Faes, Daniele Marinazzo, Sebastiano Stramaglia, 2015, Neural Networks.

Decomposition of the Transfer Entropy: Partial Conditioning and Informative Clustering

Guorong Wu, Daniele Marinazzo, Sebastiano Stramaglia, 2012, ICONIP.

Multiscale Information Decomposition Dissects Control Mechanisms of Heart Rate Variability at Rest and During Physiological Stress

Luca Faes, Daniele Marinazzo, Sebastiano Stramaglia, 2019, Entropy.

Nonlinear connectivity by Granger causality

Wei Liao, Daniele Marinazzo, Sebastiano Stramaglia, 2011, NeuroImage.

Synergy, redundancy and unnormalized Granger causality

Daniele Marinazzo, Sebastiano Stramaglia, Leonardo Angelini, 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

Guorong Wu, Wei Liao, Daniele Marinazzo, 2012, Medical Image Anal..

Recovering directed networks in neuroimaging datasets using partially conditioned Granger causality.

Guorong Wu, Wei Liao, Daniele Marinazzo, 2013, Brain connectivity.

Phase unwrapping by means of scaling information and global optimization algorithms

Alberto Refice, Fabio Bovenga, Sebastiano Stramaglia, 2003, SPIE Remote Sensing.

Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

Luca Faes, Daniele Marinazzo, Sebastiano Stramaglia, 2017, Entropy.

Interaction Information Along Lifespan of the Resting Brain Dynamics Reveals a Major Redundant Role of the Default Mode Network

Sebastiano Stramaglia, Ibai Díez, Borja Camino-Pontes, 2018, bioRxiv.

Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data

Daniele Marinazzo, Sebastiano Stramaglia, Hannelore Aerts, 2018, bioRxiv.

Synergetic and redundant information flow in dynamical systems: an operative definition based on prediction

Luca Faes, Daniele Marinazzo, Sebastiano Stramaglia, 2017, IEEE CNS 2017.

Causal Information Approach to Partial Conditioning in Multivariate Data Sets

Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia, 2011, Comput. Math. Methods Medicine.

Interferometric SAR Phase Unwrapping by Parallel Tempering on a APE100/Quadrics Supercomputer

Arcangelo Distante, Sebastiano Stramaglia, Guido Pasquariello, 1998, HPCN Europe.

Semi-supervised learning by search of optimal target vector

Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia, 2008, Pattern Recognit. Lett..

Expanding the transfer entropy to identify information subgraphs in complex systems

Guorong Wu, Daniele Marinazzo, Mario Pellicoro, 2012, EMBC.

An Invariance Property of Predictors in Kernel-Induced Hypothesis Spaces

Nicola Ancona, Sebastiano Stramaglia, S. Stramaglia, 2006, Neural Computation.

Synergy and redundancy in the Granger causal analysis of dynamical networks pdf

Daniele Marinazzo, Sebastiano Stramaglia, Jesús M. Cortés, 2014, New Journal of Physics.

Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality

Luca Faes, Daniele Marinazzo, Sebastiano Stramaglia, 2015, Neural Networks.

Local and global strategies for InSAR phase unwrapping

Alberto Refice, Nicola Veneziani, Maria Teresa Chiaradia, 1998, Remote Sensing.

Identification of redundant and synergetic circuits in triplets of electrophysiological data

Daniele Marinazzo, Sebastiano Stramaglia, Jesus M Cortes, 2014, Journal of neural engineering.

Consensus clustering approach to group brain connectivity matrices

Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia, 2016, Network Neuroscience.

Effective connectivity and cortical information flow under visual stimulation in migraine with aura

Roberto Bellotti, Daniele Marinazzo, Mario Pellicoro, 2013, 5th IEEE International Workshop on Advances in Sensors and Interfaces IWASI.

Information Flow Between Resting-State Networks

Daniele Marinazzo, Sebastiano Stramaglia, Ibai Díez, 2015, Brain Connect..

Kernel method for nonlinear granger causality.

Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia, 2007, Physical review letters.

Information flow in a network model and the law of diminishing marginal returns pdf

Guorong Wu, Daniele Marinazzo, Mario Pellicoro, 2012, ArXiv.

Information Transfer in the Brain: Insights from a Unified Approach

Daniele Marinazzo, Sebastiano Stramaglia, M. Pellicoro, 2014 .

Multiscale analysis of short term heart beat interval, arterial blood pressure, and instantaneous lung volume time series

Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia, 2007, Artif. Intell. Medicine.

Geometry Shapes Propagation: Assessing the Presence and Absence of Cortical Symmetries through a Computational Model of Cortical Spreading Depression

Luca Gerardo-Giorda, Sebastiano Stramaglia, Ibai Díez, 2016, Front. Comput. Neurosci..

Connectome preprocessing by Consensus Clustering increases separability in group neuroimaging studies

Daniele Marinazzo, Sebastiano Stramaglia, Jesus M. Cortes, 2018, bioRxiv.

Interaction information in human electrocorticography data of temporal lobe epilepsy

Guillermo J. Ortega, Daniele Marinazzo, Sebastiano Stramaglia, 2014 .

Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI.

Luca Faes, Guorong Wu, Daniele Marinazzo, 2015, IEEE transactions on bio-medical engineering.

Information Flow in Networks and the Law of Diminishing Marginal Returns: Evidence from Modeling and Human Electroencephalographic Recordings

Guorong Wu, Daniele Marinazzo, Mario Pellicoro, 2012, PloS one.

On the interpretability and computational reliability of frequency-domain Granger causality

Luca Faes, Daniele Marinazzo, Sebastiano Stramaglia, 2017, F1000Research.

Visibility graphs for fMRI data: Multiplex temporal graphs and their modulations across resting-state networks

Lucas Lacasa, Daniele Marinazzo, Sebastiano Stramaglia, 2017, bioRxiv.

Nonlinear parametric granger causality in dynamical networks

Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia, 2014 .

Causality and communities in neural networks

Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia, 2007, ESANN.

Connectome sorting by consensus clustering increases separability in group neuroimaging studies

Daniele Marinazzo, Sebastiano Stramaglia, Javier Rasero, 2019, Network Neuroscience.

Expanding the transfer entropy to identify information subgraphs in complex systems

Guorong Wu, Daniele Marinazzo, Mario Pellicoro, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Emergent Complexity from Nonlinearity, in Physics, Engineering and the Life Sciences

Sebastiano Stramaglia, Giorgio Mantica, Ruedi Stoop, 2017 .

Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data

Daniele Marinazzo, Sebastiano Stramaglia, Jesus M. Cortes, 2018 .

Use of scaling information for stochastic atmospheric absolute phase screen retrieval

Alberto Refice, Fabio Bovenga, Sebastiano Stramaglia, 2002, IEEE International Geoscience and Remote Sensing Symposium.

Identification of Informative Subgraphs in Complex Systems

Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia, 2012 .

Multiscale Granger causality analysis by à trous wavelet transform

Luca Faes, Daniele Marinazzo, Sebastiano Stramaglia, 2017, 2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI).

Multiscale analysis of information dynamics for linear multivariate processes

Luca Faes, Giandomenico Nollo, Daniele Marinazzo, 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

A Bayesian approach for the median filter in image processing

Sebastiano Stramaglia, Giovanni Sebastiani, S. Stramaglia, 1997, Signal Process..

A Neural Network with Permanent and Volatile Memory

Sebastiano Stramaglia, G. Lattanzi, Giuseppe Nardulli, 1997 .

Clustering by inhomogeneous chaotic maps in landmine detection

Sebastiano Stramaglia, Carmela Marangi, M. Pellicoro, 2001, SPIE Remote Sensing.

Measuring High-Order Interactions in Rhythmic Processes through Multivariate Spectral Information Decomposition

Ludovico Minati, Daniele Marinazzo, Luca Faes, 2021, IEEE Access.

Local Granger causality.

Luca Faes, Sebastiano Stramaglia, Tomas Scagliarini, 2021, Physical review. E.

A Controlled Thermoalgesic Stimulation Device for Exploring Novel Pain Perception Biomarkers

Sebastiano Stramaglia, Jesus M. Cortes, Borja Camino-Pontes, 2021, IEEE Journal of Biomedical and Health Informatics.

Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging

Sebastiano Stramaglia, Jesus M Cortes, Stephan P Swinnen, 2018, Human brain mapping.

Chaotic neural network clustering: an application to landmine detection by dynamic infrared imaging

Francesco De Carlo, Sebastiano Stramaglia, Giuseppe Satalino, 2001 .

Extreme brain events: Higher-order statistics of brain resting activity and its relation with structural connectivity

Sebastiano Stramaglia, Jesus M. Cortes, Dante R. Chialvo, 2015 .

Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI.

Luca Faes, Guorong Wu, Daniele Marinazzo, 2016, IEEE transactions on bio-medical engineering.

Mapping the Voxel-Wise Effective Connectome in Resting State fMRI

Wei Liao, Daniele Marinazzo, Sebastiano Stramaglia, 2013, PloS one.

Measuring randomness by leave-one-out prediction error. Analysis of EEG after painful stimulation

Nicola Ancona, Daniele Marinazzo, Sebastiano Stramaglia, 2006 .

Dynamics of neural networks with nonmonotonic neurons and adapting synapses

Sebastiano Stramaglia, S. Stramaglia, D. Caroppo, 1998 .

Persistence exponent in superantiferromagnetic quenching

Sebastiano Stramaglia, Emilio N.M. Cirillo, Giuseppe Gonnella, 1999 .

Raman spectroscopy monitoring of MCF10A cells irradiated by protons at clinical doses

Sebastiano Stramaglia, Giuseppe Perna, Vito Capozzi, 2019, International journal of radiation biology.

Steady-state visual evoked potentials in the low frequency range in migraine: a study of habituation and variability phenomena.

Mario Pellicoro, Sebastiano Stramaglia, Marina de Tommaso, 2003, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

Visually evoked phase synchronization changes of alpha rhythm in migraine: correlations with clinical features.

Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia, 2005, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

Enhanced prefrontal functional–structural networks to support postural control deficits after traumatic brain injury in a pediatric population

Daniele Marinazzo, Sebastiano Stramaglia, Stephan P. Swinnen, 2017, Network Neuroscience.

An RVB approach to the Hubbard model

Sebastiano Stramaglia, Giovanni M. Cicuta, 1992 .

Supervised algorithms for particle classification by a transition radiation detector

Sebastiano Stramaglia, Rosalia Maglietta, Roberto Bellotti, 2003 .

Lack of effects of low frequency repetitive transcranial magnetic stimulation on alpha rhythm phase synchronization in migraine patients

Sebastiano Stramaglia, Marina de Tommaso, Filippo Brighina, 2011, Neuroscience Letters.

PHASE DIAGRAM OF THE GAUGE INVARIANT TWO SPECIES ISING MODEL

Sebastiano Stramaglia, Giuseppe Gonnella, 1996 .

A matrix model for random surfaces with dynamical holes

Sebastiano Stramaglia, Luca Guido Molinari, Giovanni M. Cicuta, 1994, hep-th/9412080.

Phase ordering in chaotic map lattices with additive noise

Mario Pellicoro, Sebastiano Stramaglia, Leonardo Angelini, 2001 .

Kernel method for clustering based on optimal target vector

Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia, 2005, cond-mat/0511630.