11 citations · 15 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Pseudo-online framework for BCI evaluation: A MOABB perspective
Igor Carrara, Théodore Papadopoulo
Objective: BCI (Brain-Computer Interface) technology operates in three modes: online, offline, and pseudo-online. In the online mode, real-time EEG data is constantly analyzed. In…
Classification of BCI-EEG based on augmented covariance matrix
Igor Carrara, Théodore Papadopoulo
Objective: Electroencephalography signals are recorded as a multidimensional dataset. We propose a new framework based on the augmented covariance extracted from an autoregressive…