Blind Source Separation Methods Applied to Muscle Artefacts Removing from Epileptic Eeg Recording: A Comparative Study.

Amar Kachenoura (1, 2) , Doha Safieddine (2) , Laurent Albera (1, 2) , Gwénaël Birot (2) , Fabrice Wendling (2) , Lotfi Senhadji (1, 2) , Isabelle Merlet (2)

1 LTSI - Laboratoire Traitement du Signal et de l'Image
2 LTSI - Laboratoire Traitement du Signal et de l'Image

Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts. These artifacts obscure the EEG and complicate its interpretation or even make the interpretation unfeasible. In this paper, realistic spike EEG signals are simulated from the activation of a 5 cm2 epileptic patch in the left superior temporal gyrus. Background activities and real muscle artifacts are then added to the simulated data. We compare the efficiency of Empirical Mode Decomposition (EMD), Independent Component Analysis (ICA) and Blind Source Separation based on Canonical Correlation Analysis (BSS-CCA) to remove muscle artifacts from the EEG signals. The quantitative comparison indicates that the EMD approach exhibits a better performance than ICA and BSS-CCA, especially in the case of very low Signal to Noise Ratio (SNR).

Mots clés: ICA; BSS-CCA; EMD; muscle artefacts removing.; interictal spikes; epilepsy; muscle artefacts removing

Domaines: Ingénierie biomédicale; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Amar Kachenoura, Doha Safieddine, Laurent Albera, Gwénaël Birot, Fabrice Wendling, et al.. Blind Source Separation Methods Applied to Muscle Artefacts Removing from Epileptic Eeg Recording: A Comparative Study.. RITS 2011 (Colloque National Recherche en Imagerie et Technologies pour la Santé), Apr 2011, Rennes, France. pp.1708.1-1708.3. ⟨hal-00908765⟩