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Modelling and Data Analysis
2020. Vol. 10, no. 2, 74–92
doi:10.17759/mda.2020100206
ISSN: 2219-3758 / 2311-9454 (online)

Assessment of Bayesian Ternary Gaze Classification Algorithm (I-BDT)

 
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Abstract

I-BDT eyetracking data ternary classification (fixations, saccades, smooth pursuit) algorithm is investigated. Comparison with well-known Identification / Dispersion Threshold (I-DT) algorithm is held (accuracy, precision, recall, F1 measure). A novel approach for additionally filtering the algorithm output by distance/amplitude, area of convex hull is described.

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Keywords: smooth pursuit, classification, Bayesian decision theory, eye movements

Journal rubric: Software

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2020100206

Published

For citation: Zherdev, I.Y. (2020). Assessment of Bayesian Ternary Gaze Classification Algorithm (I-BDT). Modelling and Data Analysis, 10(2), 74–92. (In Russ.). https://doi.org/10.17759/mda.2020100206

© Zherdev I.Y., 2020

License: CC BY-NC 4.0

References

  1. Santini T., Fuhl W., Kübler T., et al. Bayesian identification of fixations, saccades, and smooth pursuits. ACM ETRA. Charleston, 2016. pp. 163–170. DOI:10.1145/2857491.2857512
  2. Nyström M., Andersson R., Holmqvist K., et al. The influence of calibration method and eye physiology on eyetracking data quality. Behav. Res. Met. 2013. Vol. 45, no 1, pp. 272–288. DOI:10.3758/s13428–012–0247–4
  3. Hooge I., Holmqvist K., Nyström M. The pupil is faster than the corneal reflection (CR): Are video based pupil-CR eye trackers suitable for studying detailed dynamics of eye movements? Vis. Res. 2016. Vol. 128, pp. 6–18. DOI:10.1016/j.visres.2016.09.002.
  4. Larsson L., Nyström M., Ardö H., et al. Smooth pursuit detection in binocular eye-tracking data with automatic video-based performance evaluation. J. Vis. 2016. Vol. 16, no 15, pp. 20. DOI:10.1167/16.15.20
  5. Startsev M., Agtzidis I., Dorr M. 1D CNN with BLSTM for automated classification of fixations, saccades, and smooth pursuits. Behav. Res. Met. 2019. Vol. 51, pp. 556–572. DOI:10.3758/s13428–018–1144–2
  6. Zemblys R., Niehorster D.C., Komogortsev O., et al. Using machine learning to detect events in eye-tracking data. Behav. Res. Met. 2018. Vol. 50, pp. 160–181. DOI:10.3758/s13428–017–0860–3
  7. Komogortsev O.V., Karpov A. Automated classification and scoring of smooth pursuit eye movements in the presence of fixations and saccades. Behav. Res. Met. 2013. Vol. 45, pp. 203–215. DOI:10.3758/s13428–012–0234–9
  8. Komogortsev O. V, Gobert D. V, Jayarathna S., et al. Standartization of automated analyses of oculomotor fixation and saccadic behaviors. IEEE Trans. Biomed. Eng. 2010. Vol. 57, no 11, pp. 2635–2645. DOI:10.1109/tbme.2010.2057429
  9. Kashyap H.J., Detorakis G., Dutt N., et al. A recurrent neural network based model of predictive smooth pursuit eye movement in primates. IEEE IJCNN. Rio de Janeiro, 2018. pp. 5353–5360. DOI:10.1109/IJCNN.2018.8489652
  10. Xivry J.J.O. de, Coppe S., Blohm G., et al. Kalman Filtering Naturally Accounts for Visually Guided and Predictive Smooth Pursuit Dynamics. J. Neurosci. 2013. Vol. 33, no 44, pp. 17301–17313. DOI:10.1523/JNEUROSCI.2321–13.2013

Information About the Authors

Ivan Y. Zherdev, associated researcher, software developer, Moscow State University of Psychology & Education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0001-6810-9297, e-mail: ivan866@mail.ru

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