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An algorithm for classifying gaze data into eye movement events using a hidden Markov model.

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gazeHMM Validation

This is the online repository for the paper "Classifying Eye Movement Events With an Unsupervised Generative Hidden Markov Model". A preprint version of the paper can be found here: https://doi.org/10.31234/osf.io/wvp2f. This repository contains the online supplementary material for the paper as well as the code to reproduce the results and the manuscript.

Structure

  • algorithm: R functions for gazeHMM algorithm
  • manuscript: files for compiling the preprint manuscript, supplementary material
  • simulation: R scripts for running the simulation study
    • preregistration: files for compiling the preregistration of the simulation study
  • validation: R scripts for running the validation of gazeHMM

Reproduction

The preprint manuscript can be reproduced by running preprint_Luken_Kucharsky_Visser_Classifying_Eye_Movement_Events.Rmd. Several files are required for the reproduction:

  • Simulation results - can be obtained by running parameter_recovery_simulation.R and parameter_recovery_simulation_exploration.R; the simulation takes a lot of time to run and thus, the results are included in the repository, i.e., part_X.Rdata and part_3_expl.Rdata; an image of R after the simulation was run is contained in results_image.Rdata
  • Raw data and fitted algorithm data for the Andersson et al. (2017) data set: Those can be obtained by placing the data of the original article in validation/data and running validation_Andersson2017.R
  • Fitted algorithm data for the Ehinger et al. (2019) data set, which can be obtained by placing the .EDF (EyeLink) files of the original article in validation/data and running validation_Ehinger2019.R

References

The references for the two validation data sets are:

Andersson, R., Larsson, L., Holmqvist, K., Stridh, M., & Nyström, M. (2017). One algorithm to rule them all? An evaluation and discussion of ten eye movement event-detection algorithms. Behavior Research Methods, 49, 616-637. https://doi.org/10.3758/s13428-016-0738-9

Ehinger, B. V., Groß, K., Ibs, I., König, P. (2019). A new comprehensive eye-tracking test battery concurrently evaluating the Pupil Labs glasses and the EyeLink 1000. PeerJ 7, e7086. https://doi.org/10.7717/peerj.7086


Check out the R package for gazeHMM under https://github.com/maltelueken/gazeHMM!

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