3 papers
eess.SP2024
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…
eess.SP2024
Enhancing Computational Efficiency of Motor Imagery BCI Classification with Block-Toeplitz Augmented Covariance Matrices and Siegel Metric
Igor Carrara, Theodore Papadopoulo
Electroencephalographic signals are represented as multidimensional datasets. We introduce an enhancement to the augmented covariance method (ACM), exploiting more thoroughly its m…
eess.SP2024
The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark
Sylvain Chevallier, Igor Carrara, Bruno Aristimunha +6
Objective. This study conduct an extensive Brain-computer interfaces (BCI) reproducibility analysis on open electroencephalography datasets, aiming to assess existing solutions and…