A
Ahmad Karfoul
Researcher at University of Rennes
Publications - 46
Citations - 445
Ahmad Karfoul is an academic researcher from University of Rennes. The author has contributed to research in topics: Blind signal separation & Independent component analysis. The author has an hindex of 7, co-authored 40 publications receiving 373 citations. Previous affiliations of Ahmad Karfoul include Al-Baath University & French Institute of Health and Medical Research.
Papers
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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.
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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.
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Iterative methods for the canonical decomposition of multi-way arrays: Application to blind underdetermined mixture identification
TL;DR: The first contribution consists in generalizing the ELS procedure to the case of complex arrays of any order greater than three, and the second is another improvement of the ALS scheme, able to profit from Hermitianity and positive semi-definiteness of the considered arrays.
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Blind Underdetermined Mixture Identification by Joint Canonical Decomposition of HO Cumulants
TL;DR: A new family of cumulant-based algorithms is proposed in order to blindly identify potentially underdetermined mixtures of statistically independent sources and to extend the concept of virtual array to the case of combination of several VAs.
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Line search and trust region strategies for canonical decomposition of semi-nonnegative semi-symmetric 3rd order tensors
TL;DR: Numerical solutions are proposed to fit the CanDecomp/ParaFac model of real three-way arrays, when the latter are both nonnegative and symmetric in two modes, and show the advantage of the optimization strategies when combined with a priori information such as partial symmetry.