Alicia Gonzalez-Moreno1, Sara Aurtenetxe2, Maria-Eugenia Lopez-Garcia3, Francisco del Pozo4, Fernando Maestu5, Angel Nevado6. 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.
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.
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