title: Saccade detection using a particle filter.
abstract: BACKGROUND
  When healthy subjects track a moving target, "catch-up" saccades are triggered to compensate for the non-perfect tracking gain. The evaluation of the pursuit and/or saccade kinematics requires that saccade and pursuit components be separated from the eye movement trace. A similar situation occurs when analyzes eye movements of patients that could contain eye drifts between saccades. This task is especially difficult, because the range of saccadic amplitudes goes from microsaccades (less than 1°) to large exploratory saccades (40°).
  
  
  METHODS
  In this paper we proposed a new algorithm to detect saccades based on a particle filter. The new method suppresses the baseline velocity component linked to smooth pursuit (or to eye drifts) and thus permits a constant threshold during a trial despite the smooth pursuit behavior. It also accounts for a wide range of saccade amplitudes.
  
  
  RESULTS
  The new method is validated with five different paradigms, microsaccades, microsaccades plus saccades with drift, linear target motion, non-linear target motion and free viewing. The sensitivity of the method to signal noise is analyzed.
  
  
  METHODS
  Traditional saccade detection algorithms using a velocity (or acceleration or jerk) threshold can be inadequate because of the baseline velocity component linked to the smooth pursuit (especially if the target motion is non-linear, i.e. not constant velocity) or to eye drifts between saccades.
  
  
  CONCLUSIONS
  The new method detects saccades in challenging situations involving eye movements between saccades (smooth pursuit and/or eye drifts) and unfiltered recordings.
authors.affiliation: ICM, Pierre et Marie Curie Paris-6 Université, INSERM UMRS 975, CNRS 7225, F-75013, Paris, France; Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD, USA. Electronic address: pierre.daye@gmail.com.
authors.forename: Pierre M
authors.initials: PM
authors.lastname: Daye
authors.affiliation: Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
authors.forename: Lance M
authors.initials: LM
authors.lastname: Optican
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001499:Bayes Theorem; D053483:Eye Movement Measurements; D006801:Humans; D017711:Nonlinear Dynamics; D011698:Pursuit, Smooth; D012438:Saccades
keywords: Bayesian estimator; Catch-up saccade; Eye drift; Microsaccades; Smooth pursuit