2 citations · 3 across the 3 of their papers we have counts for
6 papers
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…
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…
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…