title: Signal-to-noise ratio of the MEG signal after preprocessing.
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.
  
  
  METHODS
  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.
  
  
  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.
authors.affiliation: Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain. Electronic address: ali.gm88@gmail.com.
authors.forename: Alicia
authors.initials: A
authors.lastname: Gonzalez-Moreno
authors.affiliation: Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain. Electronic address: sara.aurtenexte@ctb.upm.es.
authors.forename: Sara
authors.initials: S
authors.lastname: Aurtenetxe
authors.affiliation: Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain. Electronic address: meugenia.lopez@ctb.upm.es.
authors.forename: Maria-Eugenia
authors.initials: ME
authors.lastname: Lopez-Garcia
authors.affiliation: Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain. Electronic address: francisco.delpozo@ctb.upm.es.
authors.forename: Francisco
authors.initials: F
authors.lastname: del Pozo
authors.affiliation: 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.
authors.forename: Fernando
authors.initials: F
authors.lastname: Maestu
authors.affiliation: 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.
authors.forename: Angel
authors.initials: A
authors.lastname: Nevado
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology; D060825:Cognitive Dysfunction / Q000503:physiopathology; D005071:Evoked Potentials; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D009483:Neuropsychological Tests; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D014796:Visual Perception / Q000502:physiology
keywords: Artifact; Magnetoencefalography (MEG); Noise-reduction; Preprocessing; Signal-to-noise ratio