title: Automatic BSS-based filtering of metallic interference in MEG recordings: definition and validation using simulated signals.
abstract: OBJECTIVE
  One of the principal drawbacks of magnetoencephalography (MEG) is its high sensitivity to metallic artifacts, which come from implanted intracranial electrodes and dental ferromagnetic prosthesis and produce a high distortion that masks cerebral activity. The aim of this study was to develop an automatic algorithm based on blind source separation (BSS) techniques to remove metallic artifacts from MEG signals.
  
  
  METHODS
  Three methods were evaluated: AMUSE, a second-order technique; and INFOMAX and FastICA, both based on high-order statistics. Simulated signals consisting of real artifact-free data mixed with real metallic artifacts were generated to objectively evaluate the effectiveness of BSS and the subsequent interference reduction. A completely automatic detection of metallic-related components was proposed, exploiting the known characteristics of the metallic interference: regularity and low frequency content.
  
  
  RESULTS
  The automatic procedure was applied to the simulated datasets and the three methods exhibited different performances. Results indicated that AMUSE preserved and consequently recovered more brain activity than INFOMAX and FastICA. Normalized mean squared error for AMUSE decomposition remained below 2%, allowing an effective removal of artifactual components.
  
  
  CONCLUSIONS
  To date, the performance of automatic artifact reduction has not been evaluated in MEG recordings. The proposed methodology is based on an automatic algorithm that provides an effective interference removal. This approach can be applied to any MEG dataset affected by metallic artifacts as a processing step, allowing further analysis of unusable or poor quality data.
authors.affiliation: Department of Automatic Control (ESAII), Biomedical Engineering Research Center (CREB), Universitat Politènica de Catalunya (UPC), Barcelona, Spain. Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain.
authors.forename: Carolina
authors.initials: C
authors.lastname: Migliorelli
authors.forename: Joan F
authors.initials: JF
authors.lastname: Alonso
authors.forename: Sergio
authors.initials: S
authors.lastname: Romero
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Mañanas
authors.forename: Rafał
authors.initials: R
authors.lastname: Nowak
authors.forename: Antonio
authors.initials: A
authors.lastname: Russi
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D005071:Evoked Potentials / Q000502:physiology; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008670:Metals; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
chemical_list: D008670:Metals