Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation

Escudero, Javier and Hornero, Roberto and Fernandez Perez, Alvaro and Abasolo, Daniel (2011). Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation. "Annals of Biomedical Engineering", v. 39 (n. 8); pp. 2274-2286. ISSN 0090-6964. https://doi.org/10.1007/s10439-011-0312-7.

Description

Title: Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation
Author/s:
  • Escudero, Javier
  • Hornero, Roberto
  • Fernandez Perez, Alvaro
  • Abasolo, Daniel
Item Type: Article
Título de Revista/Publicación: Annals of Biomedical Engineering
Date: August 2011
ISSN: 0090-6964
Volume: 39
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Tecnología Fotónica [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The magnetoencephalogram (MEG) is contaminated with undesired signals, which are called artifacts. Some of the most important ones are the cardiac and the ocular artifacts (CA and OA, respectively), and the power line noise (PLN). Blind source separation (BSS) has been used to reduce the influence of the artifacts in the data. There is a plethora of BSS-based artifact removal approaches, but few comparative analyses. In this study, MEG background activity from 26 subjects was processed with five widespread BSS (AMUSE, SOBI, JADE, extended Infomax, and FastICA) and one constrained BSS (cBSS) techniques. Then, the ability of several combinations of BSS algorithm, epoch length, and artifact detection metric to automatically reduce the CA, OA, and PLN were quantified with objective criteria. The results pinpointed to cBSS as a very suitable approach to remove the CA. Additionally, a combination of AMUSE or SOBI and artifact detection metrics based on entropy or power criteria decreased the OA. Finally, the PLN was reduced by means of a spectral metric. These findings confirm the utility of BSS to help in the artifact removal for MEG background activity.

More information

Item ID: 13662
DC Identifier: http://oa.upm.es/13662/
OAI Identifier: oai:oa.upm.es:13662
DOI: 10.1007/s10439-011-0312-7
Official URL: http://link.springer.com/article/10.1007/s10439-011-0312-7
Deposited by: Memoria Investigacion
Deposited on: 21 Nov 2012 09:07
Last Modified: 21 Apr 2016 12:59
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