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Features extraction from human eye movements via echo state network

  • Emerging Trends of Applied Neural Computation - E_TRAINCO
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Abstract

The paper develops a procedure for features extraction from eye movement’s time series aimed at age-related classification of humans. It exploits the properties of the echo state network (ESN) reservoir state achieved after its intrinsic plasticity tuning. A novel, recently proposed approach for ranking of dynamic data series using as single feature the length of the reservoir state vector reached after consecutive feeding of each time series into the ESN was investigated in details using eye tracker recordings of human eye movements during visual stimulation and decision-making process. Inclusion of other features like variance of ESN extracted feature for multiple similar stimulations as well as decision correctness allowed for better classification of test subjects. The results support the view that the metrics and dynamics of the eye movements depend little on age, though they are strongly related to the visual stimulation characteristics.

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Acknowledgements

The reported work is a part of and was supported by the Project No. DN02/3/2016 “Modelling of voluntary saccadic eye movements during decision making” funded by the Bulgarian Science Fund.

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Authors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Petia Koprinkova-Hristova

  2. Institute of Neurobiology, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Miroslava Stefanova, Bilyana Genova & Nadejda Bocheva

  3. South West University, Blagoevgrad, Bulgaria

    Radoslava Kraleva & Velin Kralev

Authors
  1. Petia Koprinkova-Hristova
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  2. Miroslava Stefanova
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  3. Bilyana Genova
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  4. Nadejda Bocheva
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  5. Radoslava Kraleva
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  6. Velin Kralev
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Correspondence to Petia Koprinkova-Hristova.

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Koprinkova-Hristova, P., Stefanova, M., Genova, B. et al. Features extraction from human eye movements via echo state network. Neural Comput & Applic 32, 4213–4226 (2020). https://doi.org/10.1007/s00521-019-04329-z

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