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Feasibility of Blind Source Separation Methods for the Denoising of Dense-Array EEG

Abstract : 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
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Contributor : Laurent Jonchère <>
Submitted on : Wednesday, April 20, 2016 - 11:53:37 AM
Last modification on : Friday, January 15, 2021 - 3:34:04 AM


  • HAL Id : hal-01304743, version 1



N. Taheri, Amar Kachenoura, K. Ansari-Asl, Ahmad Karfoul, Lotfi Senhadji, et al.. Feasibility of Blind Source Separation Methods for the Denoising of Dense-Array EEG. 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society, 2015, Milan, Italy. pp.4773--4776. ⟨hal-01304743⟩



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