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

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Publication Details

Author list: Fernández A, Abasolo, D, Hornero, R, Escudero, J
Publisher: Springer (part of Springer Nature): Springer Open Choice Hybrid Journals
Publication year: 2011
Volume number: 39
Issue number: 8
Start page: 2274
End page: 2286
Number of pages: 13
ISSN: 0090-6964
Languages: English-Great Britain (EN-GB)


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.


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Last updated on 2019-13-08 at 00:16