Zobrazeno 1 - 10
of 113
pro vyhledávání: '"Corsi, Marie Constance"'
In signal processing, exploring complex systems through network representations has become an area of growing interest. This study introduces the modularity graph, a new graph-based feature, to highlight the relationship across the graph communities.
Externí odkaz:
http://arxiv.org/abs/2406.15155
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
Autor:
Carrara, Igor, Aristimunha, Bruno, Corsi, Marie-Constance, de Camargo, Raphael Y., Chevallier, Sylvain, Papadopoulo, Théodore
The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision, especially in Brain-Computer Interface (BCI), where the brain activity is decoded to co
Externí odkaz:
http://arxiv.org/abs/2403.05645
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
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
Functional connectivity is a key approach to investigate oscillatory activities of the brain that provides important insights on the underlying dynamic of neuronal interactions and that is mostly applied for brain activity analysis. Building on the a
Externí odkaz:
http://arxiv.org/abs/2111.03122
Autor:
Corsi, Marie-Constance, Sorrentino, Pierpaolo, Schwartz, Denis, George, Nathalie, Gollo, Leonardo L., Chevallier, Sylvain, Hugueville, Laurent, Kahn, Ari E., Dupont, Sophie, Bassett, Danielle S., Jirsa, Viktor, De Vico Fallani, Fabrizio
Publikováno v:
In iScience 19 January 2024 27(1)
This short technical report describes the approach submitted to the Clinical BCI Challenge-WCCI2020. This submission aims to classify motor imagery task from EEG signals and relies on Riemannian Geometry, with a twist. Instead of using the classical
Externí odkaz:
http://arxiv.org/abs/2102.06015
Autor:
Cattai, Tiziana, Scarano, Gaetano, Corsi, Marie-Constance, Bassett, Danielle S., Fallani, Fabrizio De Vico, Colonnese, Stefania
Publikováno v:
IEEE transactions on Signal and Information Processing over Networks 7 (2021): 493-508
Functional connectivity (FC) can be represented as a network, and is frequently used to better understand the neural underpinnings of complex tasks such as motor imagery (MI) detection in brain-computer interfaces (BCIs). However, errors in the estim
Externí odkaz:
http://arxiv.org/abs/2012.11240