title: A statistically robust EEG re-referencing procedure to mitigate reference effect.
abstract: BACKGROUND
  The electroencephalogram (EEG) remains the primary tool for diagnosis of abnormal brain activity in clinical neurology and for in vivo recordings of human neurophysiology in neuroscience research. In EEG data acquisition, voltage is measured at positions on the scalp with respect to a reference electrode. When this reference electrode responds to electrical activity or artifact all electrodes are affected. Successful analysis of EEG data often involves re-referencing procedures that modify the recorded traces and seek to minimize the impact of reference electrode activity upon functions of the original EEG recordings.
  
  
  METHODS
  We provide a novel, statistically robust procedure that adapts a robust maximum-likelihood type estimator to the problem of reference estimation, reduces the influence of neural activity from the re-referencing operation, and maintains good performance in a wide variety of empirical scenarios.
  
  
  RESULTS
  The performance of the proposed and existing re-referencing procedures are validated in simulation and with examples of EEG recordings. To facilitate this comparison, channel-to-channel correlations are investigated theoretically and in simulation.
  
  
  METHODS
  The proposed procedure avoids using data contaminated by neural signal and remains unbiased in recording scenarios where physical references, the common average reference (CAR) and the reference estimation standardization technique (REST) are not optimal.
  
  
  CONCLUSIONS
  The proposed procedure is simple, fast, and avoids the potential for substantial bias when analyzing low-density EEG data.
authors: Boston University, Department of Mathematics and Statistics, Boston, MA, USA. Electronic address: lepage@math.bu.edu.
authors: Kyle Q
authors: KQ
authors: Lepage
authors: Boston University, Department of Mathematics and Statistics, Boston, MA, USA. Electronic address: mak@bu.edu.
authors: Mark A
authors: MA
authors: Kramer
authors: Massachusetts General Hospital, Boston, MA, USA.
authors: Catherine J
authors: CJ
authors: Chu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D016013:Likelihood Functions; D012535:Scalp / Q000502:physiology; D012815:Signal Processing, Computer-Assisted