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Inclusion of Instantaneous Influences in the Spectral Decomposition of Causality: Application to the Control Mechanisms of Heart Rate Variability.

, , , , and . EUSIPCO, page 930-934. IEEE, (2020)

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Information Flow Between Resting-State Networks., , , , , , , , and . Brain Connect., 5 (9): 554-564 (2015)Multiscale Information Decomposition Dissects Control Mechanisms of Heart Rate Variability at Rest and During Physiological Stress., , , , , , , , , and . Entropy, 21 (5): 526 (2019)Longitudinal variations of brain functional connectivity: A case report study based on a mouse model of epilepsy., , , , , , , and . F1000Research, (2015)A New Framework for the Time- and Frequency-Domain Assessment of High-Order Interactions in Networks of Random Processes., , , , , , , , , and . IEEE Trans. Signal Process., (2022)Phase unwrapping by means of scaling information and global optimization algorithms., , , and . SAR, volume 4883 of SPIE Proceedings, page 0000. SPIE, (2002)Expanding the transfer entropy to identify information subgraphs in complex systems., , , and . EMBC, page 3668-3671. IEEE, (2012)Inclusion of Instantaneous Influences in the Spectral Decomposition of Causality: Application to the Control Mechanisms of Heart Rate Variability., , , , and . EUSIPCO, page 930-934. IEEE, (2020)Quantifying High-Order Interactions in Complex Physiological Networks: A Frequency-Specific Approach., , , , and . COMPLEX NETWORKS (1), volume 1077 of Studies in Computational Intelligence, page 301-309. Springer, (2022)Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data, , , , and . bioRxiv, (2018)Causality and communities in neural networks., , , and . ESANN, page 459-464. (2007)