Microsaccade characterization using the continuous wavelet transform and principal component analysis

  • Mario Bettenbühl University of Potsdam
  • Claudia Paladini University of Potsdam
  • Konstantin Mergenthaler University of Potsdam
  • Reinhold Kliegl University of Potsdam
  • Ralf Engbert University of Potsdam
  • Matthias Holschneider University of Potsdam
Keywords: fixational eye movement, microsaccade characterization, microsaccade detection, continuous wavelet transform, principal component analysis

Abstract

During visual fixation on a target, humans perform miniature (or fixational) eye movements consisting of three components, i.e., tremor, drift, and microsaccades. Microsaccades are high velocity components with small amplitudes within fixational eye movements. However, microsaccade shapes and statistical properties vary between individual observers. Here we show that microsaccades can be formally represented with two significant shapes which we identfied using the mathematical definition of singularities for the detection of the former in real data with the continuous wavelet transform. For character-ization and model selection, we carried out a principal component analysis, which identified a step shape with an overshoot as first and a bump which regulates the overshoot as second component. We conclude that microsaccades are singular events with an overshoot component which can be detected by the continuous wavelet transform.
Published
2010-10-30
How to Cite
Bettenbühl, M., Paladini, C., Mergenthaler, K., Kliegl, R., Engbert, R., & Holschneider, M. (2010). Microsaccade characterization using the continuous wavelet transform and principal component analysis. Journal of Eye Movement Research, 3(5). https://doi.org/10.16910/jemr.3.5.1
Section
Articles