title: Automated detection and labeling of high-density EEG electrodes from structural MR images.
abstract: Accurate knowledge about the positions of electrodes in electroencephalography (EEG) is very important for precise source localizations. Direct detection of electrodes from magnetic resonance (MR) images is particularly interesting, as it is possible to avoid errors of co-registration between electrode and head coordinate systems. In this study, we propose an automated MR-based method for electrode detection and labeling, particularly tailored to high-density montages.
  
  
  
  Anatomical MR images were processed to create an electrode-enhanced image in individual space. Image processing included intensity non-uniformity correction, background noise and goggles artifact removal. Next, we defined a search volume around the head where electrode positions were detected. Electrodes were identified as local maxima in the search volume and registered to the Montreal Neurological Institute standard space using an affine transformation. This allowed the matching of the detected points with the specific EEG montage template, as well as their labeling. Matching and labeling were performed by the coherent point drift method. Our method was assessed on 8 MR images collected in subjects wearing a 256-channel EEG net, using the displacement with respect to manually selected electrodes as performance metric.
  
  
  
  Average displacement achieved by our method was significantly lower compared to alternative techniques, such as the photogrammetry technique. The maximum displacement was for more than 99% of the electrodes lower than 1 cm, which is typically considered an acceptable upper limit for errors in electrode positioning. Our method showed robustness and reliability, even in suboptimal conditions, such as in the case of net rotation, imprecisely gathered wires, electrode detachment from the head, and MR image ghosting.
  
  
  
  We showed that our method provides objective, repeatable and precise estimates of EEG electrode coordinates. We hope our work will contribute to a more widespread use of high-density EEG as a brain-imaging tool.
authors.affiliation: Neural Control of Movement Laboratory, ETH Zurich, 8057 Zurich, Switzerland. Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, UK. Laboratory of Movement Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium.
authors.forename: Marco
authors.initials: M
authors.lastname: Marino
authors.forename: Quanying
authors.initials: Q
authors.lastname: Liu
authors.forename: Silvia
authors.initials: S
authors.lastname: Brem
authors.forename: Nicole
authors.initials: N
authors.lastname: Wenderoth
authors.forename: Dante
authors.initials: D
authors.lastname: Mantini
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001331:Automation; D004566:Electrodes; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D005135:Eye Protective Devices; D006257:Head / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results