![]() ![]() To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. ![]() In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approachesĭoha Safieddine1,2, Amar Kachenoura1,2, Laurent Albera1,2, Gwénaël Birot1,2, Ahmad Karfoul3, Anca Pasnicu4, Arnaud Biraben1,2,4, Fabrice Wendling1,2, Lotfi Senhadji1,2 and Isabelle Merlet1,2*Įlectroencephalographs (EEG) recordings are often contaminated with muscle artifacts. ![]()
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