A
Amar Kachenoura
Researcher at University of Rennes
Publications - 77
Citations - 1104
Amar Kachenoura is an academic researcher from University of Rennes. The author has contributed to research in topics: Independent component analysis & Blind signal separation. The author has an hindex of 12, co-authored 74 publications receiving 965 citations. Previous affiliations of Amar Kachenoura include French Institute of Health and Medical Research.
Papers
More filters
Journal ArticleDOI
Ica: a potential tool for bci systems
TL;DR: A comparative study of widely used ICA algorithms in the BCI community, conducted on simulated electroencephalography (EEG) data, shows that an appropriate selection of an ICA algorithm may significantly improve the capabilities of BCI systems.
Journal ArticleDOI
Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches
Doha Safieddine,Doha Safieddine,Amar Kachenoura,Amar Kachenoura,Laurent Albera,Laurent Albera,Gwénaël Birot,Gwénaël Birot,Ahmad Karfoul,Anca Pasnicu,Arnaud Biraben,Arnaud Biraben,Fabrice Wendling,Fabrice Wendling,Lotfi Senhadji,Lotfi Senhadji,Isabelle Merlet,Isabelle Merlet +17 more
TL;DR: EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity and suggests that the performance of muscle artifact correction methods strongly depend on the level of data contamination, and of the source configuration underlying EEG signals.
Journal ArticleDOI
ICA-based EEG denoising: a comparative analysis of fifteen methods
Laurent Albera,Amar Kachenoura,Pierre Comon,Ahmad Karfoul,Fabrice Wendling,Lotfi Senhadji,Isabelle Merlet +6 more
TL;DR: This paper focuses on ElectroEncephaloGraphy (EEG) data denoising, and more particularly on removal of muscle artifacts from interictal epileptiform activity, and raises the question whether other ICA methods could be better suited in terms of performance and computational complexity.
Journal ArticleDOI
Multivariate empirical mode decomposition and application to multichannel filtering
Julien Fleureau,Julien Fleureau,Amar Kachenoura,Amar Kachenoura,Laurent Albera,Laurent Albera,Jean-Claude Nunes,Jean-Claude Nunes,Lotfi Senhadji,Lotfi Senhadji +9 more
TL;DR: A novel EMD approach, which allows for a straightforward decomposition of mono- and multivariate signals without any change in the core of the algorithm, is proposed, and Qualitative results illustrate the good behavior of the proposed algorithm whatever the signal dimension is.
Journal ArticleDOI
Automatic Detection and Classification of High-Frequency Oscillations in Depth-EEG Signals
Nisrine Jrad,Amar Kachenoura,Isabelle Merlet,Fabrice Bartolomei,Anca Nica,Arnaud Biraben,Fabrice Wendling +6 more
TL;DR: Experimental results show the feasibility of a robust and universal detector that has the advantages of detecting and discriminating all types of HFOs as well as avoiding false detections caused by artefacts.