Artifact removal in magnetoencephalogram background activity with independent component analysis.
The aim of this study was to assess whether independent component analysis (ICA) could be valuable to remove power line noise, cardiac, and ocular artifacts from magnetoencephalogram (MEG) background activity. The MEGs were recorded from 11 subjects with a 148-channel whole-head magnetometer. We used a statistical criterion to estimate the number of independent components. Then, a robust ICA algorithm decomposed the MEG epochs and several methods were applied to detect those artifacts. The whole process had been previously tested on synthetic data. We found that the line noise components could be easily detected by their frequency spectrum. In addition, the ocular artifacts could be identified by their frequency characteristics and scalp topography. Moreover, the cardiac artifact was better recognized by its skewness value than by its kurtosis one. Finally, the MEG signals were compared before and after artifact rejection to evaluate our method.
Escudero
Javier
J
ETS Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain. javier.escudero@ieee.org
Hornero
Roberto
R
Abásolo
Daniel
D
Fernández
Alberto
A
López-Coronado
Miguel
M
D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity