title: Influence of metallic artifact filtering on MEG signals for source localization during interictal epileptiform activity.
abstract: OBJECTIVE
  Medical intractable epilepsy is a common condition that affects 40% of epileptic patients that generally have to undergo resective surgery. Magnetoencephalography (MEG) has been increasingly used to identify the epileptogenic foci through equivalent current dipole (ECD) modeling, one of the most accepted methods to obtain an accurate localization of interictal epileptiform discharges (IEDs). Modeling requires that MEG signals are adequately preprocessed to reduce interferences, a task that has been greatly improved by the use of blind source separation (BSS) methods. MEG recordings are highly sensitive to metallic interferences originated inside the head by implanted intracranial electrodes, dental prosthesis, etc and also coming from external sources such as pacemakers or vagal stimulators. To reduce these artifacts, a BSS-based fully automatic procedure was recently developed and validated, showing an effective reduction of metallic artifacts in simulated and real signals (Migliorelli et al 2015 J. Neural Eng. 12 046001). The main objective of this study was to evaluate its effects in the detection of IEDs and ECD modeling of patients with focal epilepsy and metallic interference.
  
  
  METHODS
  A comparison between the resulting positions of ECDs was performed: without removing metallic interference; rejecting only channels with large metallic artifacts; and after BSS-based reduction. Measures of dispersion and distance of ECDs were defined to analyze the results.
  
  
  RESULTS
  The relationship between the artifact-to-signal ratio and ECD fitting showed that higher values of metallic interference produced highly scattered dipoles. Results revealed a significant reduction on dispersion using the BSS-based reduction procedure, yielding feasible locations of ECDs in contrast to the other two approaches.
  
  
  CONCLUSIONS
  The automatic BSS-based method can be applied to MEG datasets affected by metallic artifacts as a processing step to improve the localization of epileptic foci.
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: D000293:Adolescent; D000328:Adult; D016477:Artifacts; D002648:Child; D002675:Child, Preschool; D000069279:Drug Resistant Epilepsy / Q000175:diagnosis / Q000503:physiopathology*; D004567:Electrodes, Implanted; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods* / Q000592:standards; D008670:Metals; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
chemical_list: D008670:Metals