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

González Moreno, Alicia and Aurtenetxe, Sara and López García, María Eugenia and Pozo Guerrero, Francisco del and Maestú, Fernando and Nevado, Angel (2014). Signal-to-noise ratio of the MEG signal after preprocessing. "Journal of Neuroscience Methods", v. 222 ; pp. 56-61. ISSN 0165-0270. https://doi.org/10.1016/j.jneumeth.2013.10.019.

Description

Title: Signal-to-noise ratio of the MEG signal after preprocessing
Author/s:
  • González Moreno, Alicia
  • Aurtenetxe, Sara
  • López García, María Eugenia
  • Pozo Guerrero, Francisco del
  • Maestú, Fernando
  • Nevado, Angel
Item Type: Article
Título de Revista/Publicación: Journal of Neuroscience Methods
Date: January 2014
ISSN: 0165-0270
Volume: 222
Subjects:
Freetext Keywords: Magnetoencefalography (MEG); Artifact; Preprocessing; Noise-reduction; Signal-to-noise ratio
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Tecnología Fotónica [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainPSI2010-22118UnspecifiedUnspecifiedUnspecified

More information

Item ID: 26439
DC Identifier: http://oa.upm.es/26439/
OAI Identifier: oai:oa.upm.es:26439
DOI: 10.1016/j.jneumeth.2013.10.019
Official URL: http://www.sciencedirect.com/science/article/pii/S0165027013003713
Deposited by: Memoria Investigacion
Deposited on: 01 Jun 2014 08:14
Last Modified: 11 Apr 2019 09:57
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