Non-negative Tensor Factorization for Single-Channel EEG Artifact RejectionReport as inadecuate

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1 LTCI - Laboratoire Traitement et Communication de l-Information 2 PAROLE - Analysis, perception and recognition of speech Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery 3 Institut Langevin ondes et images

Abstract : New applications of Electroencephalographic recording EEG pose new challenges in terms of artifact removal. In our work, we target informed source separation methods for artifact removal in single-channel EEG recordings by exploiting prior knowledge from auxiliary lightweight sensors capturing artifactual signals. To achieve this, we first propose a method using Non-negative Matrix Factorization NMF in a Gaussian source separation that proves competitive against the classic multi-channel Independent Component Analysis ICA technique. Additionally, we confront a probabilistic Non-negative Tensor Factorization NTF with ICA, both used in an original scheme that jointly processes the EEG and auxiliary signals. The adopted NTF strategy is shown to improve separation accuracy in comparison with the usual multi-channel ICA approach and the single EEG channel NMF method.

Keywords : Gaussian model EEG artifact removal nonnegative matrix-tensor factorization source separation

Author: Cécilia Damon - Antoine Liutkus - Alexandre Gramfort - Slim Essid -



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