Rejection of artifact sources in magnetoencephalogram background activity using independent component analysis.
The aim of this pilot study was to assess the usefulness of independent component analysis (ICA) to detect cardiac artifacts and power line interferences in magnetoencephalogram (MEG) recordings. We recorded MEG signals from six subjects with a 148-channel whole-head magnetometer (MAGNES 2500 WH, 4D Neuroimaging). Epochs of 50 s with power line noise, cardiac, and ocular artifacts were selected for analysis. We applied a statistical criterion to determine the number of sources, and a robust ICA algorithm to decompose the MEG epochs. Skewness, kurtosis, and a spectral metric were used to mark the studied artifacts. We found that the power line interference could be easily detected by its frequency characteristics. Moreover, skewness outperformed kurtosis when identifying the cardiac artifact.
Escudero
Javier
J
E.T.S. Ingenieros de Telecommunicación, University of Valladolid, Spain. jescrod@ribera.tel.uva.es
Hornero
Roberto
R
Abásolo
Daniel
D
Poza
Jesús
J
Fernández
Alberto
A
López
Miguel
M
D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004574:Electromagnetic Fields; D006801:Humans; D008280:Magnetics; D015225:Magnetoencephalography / Q000295:instrumentation* / Q000379:methods*; D015233:Models, Statistical; D009482:Neurophysiology / Q000295:instrumentation / Q000379:methods; D010865:Pilot Projects; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic