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J Neurosci Methods. 2014 Jan 30;222:56-61. doi: 10.1016/j.jneumeth.2013.10.019. Epub 2013 Nov 4.

Signal-to-noise ratio of the MEG signal after preprocessing.

Author information

1
Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain. Electronic address: ali.gm88@gmail.com.
2
Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain. Electronic address: sara.aurtenexte@ctb.upm.es.
3
Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain. Electronic address: meugenia.lopez@ctb.upm.es.
4
Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain. Electronic address: francisco.delpozo@ctb.upm.es.
5
Basic Psychology Department II, School of Psychology, Complutense University of Madrid, Campus de Somosaguas, 28223 Madrid, Spain; Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain. Electronic address: fernando.maestu@ctb.upm.es.
6
Basic Psychology Department II, School of Psychology, Complutense University of Madrid, Campus de Somosaguas, 28223 Madrid, Spain; Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain. Electronic address: angel.nevado@psi.ucm.es.

Abstract

BACKGROUND:

Magnetoencephalography (MEG) provides a direct measure of brain activity with high combined spatiotemporal resolution. Preprocessing is necessary to reduce contributions from environmental interference and biological noise.

NEW METHOD:

The effect on the signal-to-noise ratio of different preprocessing techniques is evaluated. The signal-to-noise ratio (SNR) was defined as the ratio between the mean signal amplitude (evoked field) and the standard error of the mean over trials.

RESULTS:

Recordings from 26 subjects obtained during and event-related visual paradigm with an Elekta MEG scanner were employed. Two methods were considered as first-step noise reduction: Signal Space Separation and temporal Signal Space Separation, which decompose the signal into components with origin inside and outside the head. Both algorithm increased the SNR by approximately 100%. Epoch-based methods, aimed at identifying and rejecting epochs containing eye blinks, muscular artifacts and sensor jumps provided an SNR improvement of 5-10%. Decomposition methods evaluated were independent component analysis (ICA) and second-order blind identification (SOBI). The increase in SNR was of about 36% with ICA and 33% with SOBI.

COMPARISON WITH EXISTING METHODS:

No previous systematic evaluation of the effect of the typical preprocessing steps in the SNR of the MEG signal has been performed.

CONCLUSIONS:

The application of either SSS or tSSS is mandatory in Elekta systems. No significant differences were found between the two. While epoch-based methods have been routinely applied the less often considered decomposition methods were clearly superior and therefore their use seems advisable.

KEYWORDS:

Artifact; Magnetoencefalography (MEG); Noise-reduction; Preprocessing; Signal-to-noise ratio

PMID:
24200506
DOI:
10.1016/j.jneumeth.2013.10.019
[Indexed for MEDLINE]

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