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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 7489108, 10 pages
http://dx.doi.org/10.1155/2016/7489108
Research Article

Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study

1Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and Royal Academy of Music, Aarhus/Aalborg, Nørrebrogade 44, Building 10G, 4th and 5th floor, 8000 Aarhus C, Denmark
2Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, 00076 Aalto, Finland
3Cognitive Brain Research Unit, Institute of Behavioural Sciences, University of Helsinki, P.O. Box 9 (Siltavuorenpenger 1 B), 00014 Helsinki, Finland
4BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, P.O. Box 340, Hospital District of Helsinki and Uusimaa, 00029 Helsinki, Finland

Received 29 April 2016; Accepted 23 June 2016

Academic Editor: Victor H. C. de Albuquerque

Copyright © 2016 Niels Trusbak Haumann et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal—slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the waveform when the signal-to-noise ratio (SNR) in the original data is relatively low—in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR.