Lotfi Senhadji

TL;DR: In this paper, the authors used wavelet transforms to describe and recognize isolated cardiac beats and evaluated their capability of discriminating between normal, premature ventricular contraction, and ischemic beats by means of linear discriminant analysis.

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.

TL;DR: Three families of methods (linear and nonlinear regression, phase synchronization, and generalized synchronization) are reviewed and it is recommended to first apply these "robust" methods in order to characterize brain connectivity before using more sophisticated methods that require specific assumptions about the underlying model of relationship.

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.

TL;DR: A comprehensive comparison of different classes of methods (linear and nonlinear regressions, phase synchronization, and generalized synchronization) based on various simulation models is proposed and shows that the performances of the compared methods are highly dependent on the hypothesis regarding the underlying model for the generation of the signals.