title: Removing muscle and eye artifacts using blind source separation techniques in ictal EEG source imaging.
abstract: OBJECTIVE
  The contamination of muscle and eye artifacts during an ictal period of the EEG significantly distorts source estimation algorithms. Recent blind source separation (BSS) techniques based on canonical correlation (BSS-CCA) and independent component analysis with spatial constraints (SCICA) have shown much promise in the removal of these artifacts. In this study we want to use BSS-CCA and SCICA as a preprocessing step before the source estimation during the ictal period.
  
  
  METHODS
  Both the contaminated and cleaned ictal EEG were subjected to the RAP-MUSIC algorithm. This is a multiple dipole source estimation technique based on the separation of the EEG in signal and noise subspace. The source estimates were compared with the subtracted ictal SPECT (iSPECT) coregistered to magnetic resonance imaging (SISCOM) by means of the euclidean distance between the iSPECT activations and the dipole location estimates. SISCOM results in an image denoting the ictal onset zone with a propagation.
  
  
  RESULTS
  We applied the artifact removal and the source estimation on 8 patients. Qualitatively, we can see that 5 out of 8 patients show an improvement of the dipoles. The dipoles are nearer to or have tighter clusters near the iSPECT activation. From the median of the distance measure, we could appreciate that 5 out of 8 patients show improvement.
  
  
  CONCLUSIONS
  The results show that BSS-CCA and SCICA can be applied to remove artifacts, but the results should be interpreted with care. The results of the source estimation can be misleading due to excessive noise or modeling errors. Therefore, the accuracy of the source estimation can be increased by preprocessing the ictal EEG segment by BSS-CCA and SCICA.
  
  
  CONCLUSIONS
  This is a pilot study where EEG source localization in the presurgical evaluation can be made more reliable, if preprocessing techniques such as BSS-CCA and SCICA are used prior to EEG source analysis on ictal episodes.
authors.affiliation: Department of Electronics and Information Systems, MEDISIP-IBBT-IBITECH, Ghent University, De Pintelaan 185, 9000 Ghent, Belgium. hans.hallez@UGent.be
authors.forename: H
authors.initials: H
authors.lastname: Hallez
authors.forename: M
authors.initials: M
authors.lastname: De Vos
authors.forename: B
authors.initials: B
authors.lastname: Vanrumste
authors.forename: P
authors.initials: P
authors.lastname: Van Hese
authors.forename: S
authors.initials: S
authors.lastname: Assecondi
authors.forename: K
authors.initials: K
authors.lastname: Van Laere
authors.forename: P
authors.initials: P
authors.lastname: Dupont
authors.forename: W
authors.initials: W
authors.lastname: Van Paesschen
authors.forename: S
authors.initials: S
authors.lastname: Van Huffel
authors.forename: I
authors.initials: I
authors.lastname: Lemahieu
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001767:Blinking / Q000502:physiology*; D001931:Brain Mapping; D004569:Electroencephalography / Q000379:methods*; D004828:Epilepsies, Partial / Q000473:pathology / Q000503:physiopathology*; D005133:Eye Movements / Q000502:physiology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008297:Male; D008875:Middle Aged; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology; D010865:Pilot Projects