title: EEG source localization: Sensor density and head surface coverage.
abstract: BACKGROUND
  The accuracy of EEG source localization depends on a sufficient sampling of the surface potential field, an accurate conducting volume estimation (head model), and a suitable and well-understood inverse technique. The goal of the present study is to examine the effect of sampling density and coverage on the ability to accurately localize sources, using common linear inverse weight techniques, at different depths. Several inverse methods are examined, using the popular head conductivity.
  
  
  METHODS
  Simulation studies were employed to examine the effect of spatial sampling of the potential field at the head surface, in terms of sensor density and coverage of the inferior and superior head regions. In addition, the effects of sensor density and coverage are investigated in the source localization of epileptiform EEG.
  
  
  RESULTS
  Greater sensor density improves source localization accuracy. Moreover, across all sampling density and inverse methods, adding samples on the inferior surface improves the accuracy of source estimates at all depths.
  
  
  METHODS
  More accurate source localization of EEG data can be achieved with high spatial sampling of the head surface electrodes.
  
  
  CONCLUSIONS
  The most accurate source localization is obtained when the voltage surface is densely sampled over both the superior and inferior surfaces.
authors: Electrical Geodesics, Inc., Eugene, OR, USA. Electronic address: jsong@egi.com.
authors: Jasmine
authors: J
authors: Song
authors: Electrical Geodesics, Inc., Eugene, OR, USA.
authors: Colin
authors: C
authors: Davey
authors: Electrical Geodesics, Inc., Eugene, OR, USA.
authors: Catherine
authors: C
authors: Poulsen
authors: Electrical Geodesics, Inc., Eugene, OR, USA; Department of Psychology, University of Oregon, Eugene, OR, USA.
authors: Phan
authors: P
authors: Luu
authors: Electrical Geodesics, Inc., Eugene, OR, USA; Neuroinformatics Center, University of Oregon, Eugene, OR, USA.
authors: Sergei
authors: S
authors: Turovets
authors: Electrical Geodesics, Inc., Eugene, OR, USA.
authors: Erik
authors: E
authors: Anderson
authors: Electrical Geodesics, Inc., Eugene, OR, USA.
authors: Kai
authors: K
authors: Li
authors: Electrical Geodesics, Inc., Eugene, OR, USA; Department of Psychology, University of Oregon, Eugene, OR, USA.
authors: Don
authors: D
authors: Tucker
mesh_terms: D001921:Brain / Q000502:physiology / Q000503:physiopathology; D001931:Brain Mapping / Q000295:instrumentation* / Q000379:methods*; D003198:Computer Simulation; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology; D006257:Head / Q000502:physiology; D006801:Humans; D008279:Magnetic Resonance Imaging; D008954:Models, Biological
keywords: Dense array EEG; Epilepsy; FDM; Minimum norm; SLORETA; Source localization