Title :
Artifact Removal in Magnetoencephalogram Background Activity With Independent Component Analysis
Author :
Escudero, Javier ; Hornero, Roberto ; Abásolo, Daniel ; Fernández, Alberto ; López-Coronado, Miguel
Author_Institution :
Valladolid Univ., Valladolid
Abstract :
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.
Keywords :
independent component analysis; magnetoencephalography; magnetometers; medical signal processing; neurophysiology; 148-channel whole-head magnetometer; ICA algorithm; MEG background activity; artifact removal; cardiac artifacts; independent component analysis; kurtosis value; magnetoencephalogram background activity; ocular artifacts; power line noise; scalp topography; skewness value; Electrooculography; Frequency; Independent component analysis; Magnetic field measurement; Magnetic shielding; Principal component analysis; SQUIDs; Scalp; Superconducting device noise; Superconducting magnets; Artifact rejection; higher order statistics; independent component analysis (ICA); magnetoencephalography (MEG); Algorithms; Artifacts; Brain; Computer Simulation; Data Interpretation, Statistical; Diagnosis, Computer-Assisted; Humans; Magnetoencephalography; Models, Neurological; Models, Statistical; Principal Component Analysis; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2007.894968