An improved algorithm for automatic detection of saccades in eye movement data and for calculating saccade parameters.
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.
Behrens
F
F
Otto-van-Guericke University Magdeburg, Institute for Distributed Systems, Department of Embedded Systems and Operating Systems, Universitätsplatz 2, D-39106 Magdeburg, Germany. franklwbehrens@gmail.com
Mackeben
M
M
Schröder-Preikschat
W
W
D000222:Adaptation, Physiological / Q000502:physiology; D000465:Algorithms; D001331:Automation; D001334:Automobile Driving / Q000523:psychology; D002138:Calibration; D003625:Data Collection / Q000379:methods; D003627:Data Interpretation, Statistical; D004585:Electrooculography; D005403:Fixation, Ocular; D006801:Humans; D012438:Saccades / Q000502:physiology*; D012890:Sleep / Q000502:physiology