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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This study was partially supported by the “Ministerio de Ciencia e Innovación” and FEDER grant TEC2008-02241. We are thankful to the three Reviewers of this manuscript for their useful comments.
Conflict of interest
There are no conflicts of interest that could inappropriately influence this manuscript.
Associate Editor Nathalie Virag oversaw the review of this article.
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Escudero, J., Hornero, R., Abásolo, D. et al. Quantitative Evaluation of Artifact Removal in Real Magnetoencephalogram Signals with Blind Source Separation. Ann Biomed Eng 39, 2274–2286 (2011). https://doi.org/10.1007/s10439-011-0312-7
- Blind source separation
- Cardiac artifact
- Independent component analysis
- Ocular artifact
- Power line noise