Behavior Research Methods

, Volume 42, Issue 3, pp 701–708 | Cite as

An improved algorithm for automatic detection of saccades in eye movement data and for calculating saccade parameters

  • F. BehrensEmail author
  • M. MacKeben
  • W. Schröder-Preikschat


This analysis of time series of eye movements is a saccade-detection algorithm that is based on an earlier algorithm. It achieves substantial improvements by using an adaptive-threshold model instead of fixed thresholds and using the eye-movement acceleration signal. This has four advantages: (1) Adaptive thresholds are calculated automatically from the preceding acceleration data for detecting the beginning of a saccade, and thresholds are modified during the saccade. (2) The monotonicity of the position signal during the saccade, together with the acceleration with respect to the thresholds, is used to reliably determine the end of the saccade. (3) This allows differentiation between saccades following the main-sequence and non-main-sequence saccades. (4) Artifacts of various kinds can be detected and eliminated. The algorithm is demonstrated by applying it to human eye movement data (obtained by EOG) recorded during driving a car. A second demonstration of the algorithm detects microsleep episodes in eye movement data.


Acceleration Signal Position Signal Adaptive Threshold Finite Impulse Response Filter Constant False Alarm Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Psychonomic Society, Inc. 2010

Authors and Affiliations

  • F. Behrens
    • 3
    Email author
  • M. MacKeben
    • 1
  • W. Schröder-Preikschat
    • 2
  1. 1.Smith-Kettlewell Eye Research InstituteSan Francisco
  2. 2.University of Erlangen-NurembergNurembergGermany
  3. 3.Institute for Distributed Systems, Department of Embedded Systems and Operating SystemsOtto-van-Guericke University MagdeburgMagdeburgGermany

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