Search Results for author:
Found 8 papers, 3 papers with code
Geometric Neural Network based on Phase Space for BCI decoding
\textbf{Approach:} Our research aims to develop a DL algorithm that delivers effective results with a limited number of electrodes.
HappyFeat -- An interactive and efficient BCI framework for clinical applications
We also show that it can be used as an efficient tool for comparing different metrics extracted from the signals, to train the classification algorithm.
Intentional binding enhances hybrid BCI control
Mental imagery-based brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system.
Functional connectivity ensemble method to enhance BCI performance (FUCONE)
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.
RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020
This short technical report describes the approach submitted to the Clinical BCI Challenge-WCCI2020.
Improving J-divergence of brain connectivity states by graph Laplacian denoising
Using our novel formulation of the J-divergence, we are able to quantify the distance between the FC networks in the motor imagery and resting states, as well as to understand the contribution of each Laplacian variable to the total J-divergence between two states.
BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks
Brain-computer interfaces (BCIs) constitute a promising tool for communication and control.
Phase/amplitude synchronization of brain signals during motor imagery BCI tasks
In the last decade, functional connectivity (FC) estimators have been increasingly explored based on their ability to capture synchronization between multivariate brain signals.
