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

Escudero, Javier; Hornero, Roberto; Fernandez Perez, Alvaro y 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.

Descripción

Título: Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation
Autor/es:
  • Escudero, Javier
  • Hornero, Roberto
  • Fernandez Perez, Alvaro
  • Abasolo, Daniel
Tipo de Documento: Artículo
Título de Revista/Publicación: Annals of Biomedical Engineering
Fecha: Agosto 2011
Volumen: 39
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Tecnología Fotónica [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 13662
Identificador DC: http://oa.upm.es/13662/
Identificador OAI: oai:oa.upm.es:13662
Identificador DOI: 10.1007/s10439-011-0312-7
URL Oficial: http://link.springer.com/article/10.1007/s10439-011-0312-7
Depositado por: Memoria Investigacion
Depositado el: 21 Nov 2012 09:07
Ultima Modificación: 21 Abr 2016 12:59
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