%0 Conference Proceedings %T Feasibility of Blind Source Separation Methods for the Denoising of Dense-Array EEG %+ Shahid Chamran University of Ahvaz (SCU) %+ Laboratoire Traitement du Signal et de l'Image (LTSI) %+ Laboratoire Traitement du Signal et de l'Image (LTSI) %A Taheri, N. %A Kachenoura, Amar %A Ansari-Asl, K. %A Karfoul, Ahmad %A Senhadji, Lotfi %A Albera, Laurent %A Merlet, I %< avec comité de lecture %Z WOS:000371717205014 %B 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society %C Milan, Italy %I IEEE %P 4773--4776 %8 2015 %D 2015 %K algorithm %K Artifacts %K electrodes %K Epilepsy %K ICA %K independent component analysis %K localization %Z Life Sciences [q-bio]/BioengineeringConference papers %X High-density electroencephalographic recordings have recently been proved to bring useful information during the pre-surgical evaluation of patients suffering from drug-resistant epilepsy. However, these recordings can be particularly obscured by noise and artifacts. This paper focuses on the denoising of dense-array EEG data (e.g. 257 channels) contaminated with muscle artifacts. In this context, we compared the efficiency of several Independent Component Analysis (ICA) methods, namely SOBI, SOBIrob, PICA, InfoMax, two different implementations of FastICA, COM2, ERICA, and SIMBEC, as well as that of Canonical Correlation Analysis (CCA). We evaluated the performance using the Normalized Mean Square Error (NMSE) criterion and calculated the numerical complexity. Quantitative results obtained on realistic simulated data show that some of the ICA methods as well as CCA can properly remove muscular artifacts from dense-array EEG %G English %L hal-01304743 %U https://univ-rennes.hal.science/hal-01304743 %~ UNIV-RENNES1 %~ LTSI %~ STATS-UR1 %~ UR1-HAL %~ UR1-MATH-STIC %~ TEST-UNIV-RENNES %~ TEST-UR-CSS %~ UNIV-RENNES %~ UR1-MATH-NUM %~ UR1-BIO-SA