7 citations · 24 across the 16 of their papers we have counts for
17 papers
Neuronal avalanches as a predictive biomarker of BCI performance: towards a tool to guide tailored training program
Camilla Mannino, Pierpaolo Sorrentino, Mario Chavez +1
Brain-Computer Interfaces (BCIs) based on motor imagery (MI) hold promise for restoring control in individuals with motor impairments. However, up to 30% of users remain unable to…
EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Bruno Aristimunha, Dung Truong, Pierre Guetschel +16
Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-ba…
Introducing the modularity graph: an application to brain functional networks
Tiziana Cattai, Camilla Caporali, Marie-Constance Corsi +1
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-base…
Community Detection from Multiple Observations: from Product Graph Model to Brain Applications
Tiziana Cattai, Gaetano Scarano, Marie-Constance Corsi +2
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 inf…
Geometric Neural Network based on Phase Space for BCI-EEG decoding
Igor Carrara, Bruno Aristimunha, Marie-Constance Corsi +3
Objective: 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. This i…
Low-dimensional controllability of brain networks
Remy Ben Messaoud, Vincent Le Du, Brigitte Charlotte Kaufmann +5
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. How…
Intentional binding enhances hybrid BCI control
Tristan Venot, Arthur Desbois, Marie-Constance Corsi +3
Mental imagery-based brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system. Making BCIs efficient and a…
HappyFeat -- An interactive and efficient BCI framework for clinical applications
Arthur Desbois, Tristan Venot, Fabrizio De Vico Fallani +1
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 t…
Node-layer duality in networked systems
Charley Presigny, Marie-Constance Corsi, Fabrizio De Vico Fallani
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…
Functional connectivity ensemble method to enhance BCI performance (FUCONE)
Marie-Constance Corsi, Sylvain Chevallier, Fabrizio De Vico Fallani +1
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…
RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020
Marie-Constance Corsi, Florian Yger, Sylvain Chevallier +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…
Improving J-divergence of brain connectivity states by graph Laplacian denoising
Tiziana Cattai, Gaetano Scarano, Marie-Constance Corsi +3
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) detec…
BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks
Marie-Constance Corsi, Mario Chavez, Denis Schwartz +6
Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is diff…
Network-based brain computer interfaces: principles and applications
Juliana Gonzalez-Astudillo, Tiziana Cattai, Giulia Bassignana +2
Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basi…
Phase/amplitude synchronization of brain signals during motor imagery BCI tasks
Tiziana Cattai, Stefania Colonnese, Marie-Constance Corsi +3
The extraction of brain functioning features is a crucial step in the definition of brain-computer interfaces (BCIs). In the last decade, functional connectivity (FC) estimators ha…
Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior
Jennifer Stiso, Marie-Constance Corsi, Jean M. Vettel +5
Motor imagery-based brain-computer interfaces (BCIs) use an individuals ability to volitionally modulate localized brain activity as a therapy for motor dysfunction or to probe cau…
Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces
Marie-Constance Corsi, Mario Chavez, Denis Schwartz +4
We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) signals to improve classification p…