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The exploration of brain-heart interactions within various paradigms, including affective computing, human-computer interfaces, and sensorimotor evaluation, stands as a significant milestone in biomarker development and neuroscientific research. A ra
Externí odkaz:
http://arxiv.org/abs/2409.15835
Autor:
Longhena, Alice, Guillemaud, Martin, Fallani, Fabrizio De Vico, Migliaccio, Raffaella Lara, Chavez, Mario
Graph theoretical methods have proven valuable for investigating alterations in both anatomical and functional brain connectivity networks during Alzheimer's disease (AD). Recent studies suggest that representing brain networks in a suitable geometri
Externí odkaz:
http://arxiv.org/abs/2407.16589
Brain-computer interfaces (BCIs) enable users to interact with the external world using brain activity. Despite their potential in neuroscience and industry, BCI performance remains inconsistent in noninvasive applications, often prioritizing algorit
Externí odkaz:
http://arxiv.org/abs/2407.11617
Autor:
Cattai, Tiziana, Scarano, Gaetano, Corsi, Marie-Constance, Fallani, Fabrizio De Vico, Colonnese, Stefania
This paper proposes a multilayer graph model for the community detection from multiple observations. This is a very frequent situation, when different estimators are applied to infer graph edges from signals at its nodes, or when different signal mea
Externí odkaz:
http://arxiv.org/abs/2406.15142
Network visualization is essential for many scientific, societal, technological and artistic domains. The primary goal is to highlight patterns out of nodes interconnected by edges that are easy to understand, facilitate communication and support dec
Externí odkaz:
http://arxiv.org/abs/2406.10717
Autor:
Messaoud, Remy Ben, Du, Vincent Le, Kaufmann, Brigitte Charlotte, Couvy-Duchesne, Baptiste, Migliaccio, Lara, Bartolomeo, Paolo, Chavez, Mario, Fallani, Fabrizio De Vico
Network controllability is a powerful tool to study causal relationships in complex systems and identify the driver nodes for steering the network dynamics into desired states. However, due to ill-posed conditions, results become unreliable when the
Externí odkaz:
http://arxiv.org/abs/2311.11132
Brain-Computer Interface (BCI) systems allow users to perform actions by translating their brain activity into commands. Such systems usually need a training phase, consisting in training a classification algorithm to discriminate between mental stat
Externí odkaz:
http://arxiv.org/abs/2310.02948
Autor:
Venot, Tristan, Desbois, Arthur, Corsi, Marie-Constance, Hugueville, Laurent, Saint-Bauzel, Ludovic, Fallani, Fabrizio De Vico
Mental imagery-based brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system. Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clini
Externí odkaz:
http://arxiv.org/abs/2309.12195
The stochastic exploration of the configuration space and the exploitation of functional states underlie many biological processes. The evolutionary dynamics stands out as a remarkable example. Here, we introduce a novel formalism that mimics evoluti
Externí odkaz:
http://arxiv.org/abs/2306.17300
Publikováno v:
Nat Commun 15, 6038 (2024)
Real-world networks typically exhibit several aspects, or layers, of interactions among their nodes. By permuting the role of the nodes and the layers, we establish a new criterion to construct the dual of a network. This approach allows to examine i
Externí odkaz:
http://arxiv.org/abs/2306.12136