108 citations · 122 across the 6 of their papers we have counts for
7 papers
Improved Riemannian potato field: an Automatic Artifact Rejection Method for EEG
Davoud Hajhassani, Quentin Barthélemy, Jérémie Mattout +1
Electroencephalography (EEG) signal cleaning has long been a critical challenge in the research community. The presence of artifacts can significantly degrade EEG data quality, com…
Minimizing subject-dependent calibration for BCI with Riemannian transfer learning
Salim Khazem, Sylvain Chevallier, Quentin Barthélemy +2
Calibration is still an important issue for user experience in Brain-Computer Interfaces (BCI). Common experimental designs often involve a lengthy training period that raises the…
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations
Yoann Isaac, Quentin Barthélemy, Cédric Gouy-Pailler +2
This paper addresses the structurally-constrained sparse decomposition of multi-dimensional signals onto overcomplete families of vectors, called dictionaries. The contribution of…
Using Riemannian geometry for SSVEP-based Brain Computer Interface
Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthelemy
Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results. Studying electroencephalographic (EEG) signals f…
Multivariate Temporal Dictionary Learning for EEG
Quentin Barthélemy, Cédric Gouy-Pailler, Yoann Isaac +3
This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG s…
Multi-dimensional sparse structured signal approximation using split Bregman iterations
Yoann Isaac, Quentin Barthélemy, Jamal Atif +2
The paper focuses on the sparse approximation of signals using overcomplete representations, such that it preserves the (prior) structure of multi-dimensional signals. The underlyi…
Metrics for Multivariate Dictionaries
Sylvain Chevallier, Quentin Barthélemy, Jamal Atif
Overcomplete representations and dictionary learning algorithms kept attracting a growing interest in the machine learning community. This paper addresses the emerging problem of c…