Cleaning MEG artifacts using external cues

Using EEG, ECoG, MEG, and microelectrodes to record brain activity is prone to multiple artifacts. The main power line (mains line), video equipment, mechanical vibrations and activities outside the brain are the most common sources of artifacts. MEG amplitudes are low, and even small artifacts distort recordings. In this study, we show how these artifacts can be efficiently removed by recording external cues during MEG recordings. These external cues are subsequently used to register the precise times or spectra of the artifacts. The results indicate that these procedures preserve both the spectra and the time domain wave-shapes of the neuromagnetic signal, while successfully reducing the contribution of the artifacts to the target signals without reducing the rank of the data.

[1]  S. Taulu,et al.  Suppression of Interference and Artifacts by the Signal Space Separation Method , 2003, Brain Topography.

[2]  M. Schiek,et al.  Detection of Artifacts and Brain Responses Using Instantaneous Phase Statistics in Independent Components , 2011 .

[3]  R. Ilmoniemi,et al.  Signal-space projection method for separating MEG or EEG into components , 1997, Medical and Biological Engineering and Computing.

[4]  R. Ilmoniemi,et al.  Design, construction, and performance of a large-volume magnetic shield , 1982 .

[5]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[6]  J. Navarro-Pedreño Numerical Methods for Least Squares Problems , 1996 .

[7]  Uwe Pietrzyk,et al.  Integration of Amplitude and Phase Statistics for Complete Artifact Removal in Independent Components of Neuromagnetic Recordings , 2008, IEEE Transactions on Biomedical Engineering.

[8]  Erkki Oja,et al.  Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings , 1997, NIPS.

[9]  Erkki Oja,et al.  Independent component approach to the analysis of EEG and MEG recordings , 2000, IEEE Transactions on Biomedical Engineering.

[10]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[11]  M. Abeles Quantification, smoothing, and confidence limits for single-units' histograms , 1982, Journal of Neuroscience Methods.

[12]  Jonathan Z. Simon,et al.  Abstract Journal of Neuroscience Methods 165 (2007) 297–305 Denoising based on time-shift PCA , 2007 .

[13]  D. Cohen Magnetoencephalography: Detection of the Brain's Electrical Activity with a Superconducting Magnetometer , 1972, Science.

[14]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[15]  V. Jousmäki,et al.  Cardiac artifacts in magnetoencephalogram. , 1996, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[16]  Roberto Hornero,et al.  Quantitative Evaluation of Artifact Removal in Real Magnetoencephalogram Signals with Blind Source Separation , 2011, Annals of Biomedical Engineering.

[17]  S. Taulu,et al.  Presentation of electromagnetic multichannel data: The signal space separation method , 2005 .

[18]  Se Robinson,et al.  Functional neuroimaging by Synthetic Aperture Magnetometry (SAM) , 1999 .

[19]  Peter C. Hansen,et al.  MEG. An introduction to methods , 2010 .