L
Laurent Albera
Researcher at University of Rennes
Publications - 145
Citations - 2722
Laurent Albera 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 28, co-authored 143 publications receiving 2445 citations. Previous affiliations of Laurent Albera include French Institute of Health and Medical Research & Thales Communications.
Papers
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On the virtual array concept for higher order array processing
TL;DR: The purpose of this paper is to provide some important insights into the mechanisms and to both the resolution and the maximal processing capacity, of numerous 2qth order array processing methods by extending the Virtual Array concept to an arbitrary even order for several arrangements of the data statistics and for arrays with space, angular and/or polarization diversity.
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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.
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High-Resolution Direction Finding From Higher Order Statistics: The $2rm q$ -MUSIC Algorithm
TL;DR: An extension of the MUSIC method to an arbitrary even order 2q (qges1), giving rise to the 2q-MUSIC methods, which show off new important results for direction-finding applications and in particular the best performances of 2-M USIC and 4-M MUSIC methods with q>2, despite their higher variance, when some resolution is required.
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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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Blind Identification of Overcomplete MixturEs of sources (BIOME)
TL;DR: In this paper, the blind identification of linear mixtures of independent random processes is related to the diagonalization of some tensors, and the problem is posed in terms of a non-conventional joint approximate diagonalization.