Quantitative Evaluation of Artifact Removal in Real Magnetoencephalogram Signals with Blind Source Separation
Open Access
- 22 April 2011
- journal article
- research article
- Published by Springer Nature in Annals of Biomedical Engineering
- Vol. 39 (8) , 2274-2286
- https://doi.org/10.1007/s10439-011-0312-7
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.Keywords
This publication has 36 references indexed in Scilit:
- Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source SeparationAnnals of Biomedical Engineering, 2008
- Enhanced automatic artifact detection based on independent component analysis and Renyi’s entropyNeural Networks, 2008
- Improving MEG source localizations: An automated method for complete artifact removal based on independent component analysisNeuroImage, 2008
- An automatic identification and removal method for eye-blink artifacts in event-related magnetoencephalographic measurementsPhysiological Measurement, 2007
- Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysisNeuroImage, 2007
- Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approachPhysiological Measurement, 2006
- Approach and Applications of Constrained ICAIEEE Transactions on Neural Networks, 2005
- Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signalsClinical Neurophysiology, 2004
- Cardiac Artifacts in MagnetoencephalogramJournal Of Clinical Neurophysiology, 1996
- Indeterminacy and identifiability of blind identificationIEEE Transactions on Circuits and Systems, 1991