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Generative Modeling of Synthetic Eye-tracking Data: NLP-based Approach with Recurrent Neural Networks

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Neural based Implementation, Applications and Solutions; Recurrent Neural Networks

Authors: Mahmoud Elbattah 1 ; Jean-Luc Guérin 1 ; Romuald Carette 1 ; Federica Cilia 2 and Gilles Dequen 1

Affiliations: 1 Laboratoire MIS, Université de Picardie Jules Verne, Amiens, France Laboratoire CRP-CPO, Université de Picardie Jules Verne, Amiens, France ; 2 Laboratoire CRP-CPO, Université de Picardie Jules Verne, Amiens, France

Keyword(s): Eye-tracking, Machine Learning, Recurrent Neural Networks, NLP.

Abstract: This study explores a Machine Learning-based approach for generating synthetic eye-tracking data. In this respect, a novel application of Recurrent Neural Networks is experimented. Our approach is based on learning the sequence patterns of eye-tracking data. The key idea is to represent eye-tracking records as textual strings, which describe the sequences of fixations and saccades. The study therefore could borrow methods from the Natural Language Processing (NLP) domain for transforming the raw eye-tracking data. The NLP-based transformation is utilised to convert the high-dimensional eye-tracking data into an amenable representation for learning. Furthermore, the generative modeling could be implemented as a task of text generation. Our empirical experiments support further exploration and development of such NLP-driven approaches for the purpose of producing synthetic eye-tracking datasets for a variety of potential applications.

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Paper citation in several formats:
Elbattah, M.; Guérin, J.; Carette, R.; Cilia, F. and Dequen, G. (2020). Generative Modeling of Synthetic Eye-tracking Data: NLP-based Approach with Recurrent Neural Networks. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - NCTA; ISBN 978-989-758-475-6; ISSN 2184-3236, SciTePress, pages 479-484. DOI: 10.5220/0010177204790484

@conference{ncta20,
author={Mahmoud Elbattah. and Jean{-}Luc Guérin. and Romuald Carette. and Federica Cilia. and Gilles Dequen.},
title={Generative Modeling of Synthetic Eye-tracking Data: NLP-based Approach with Recurrent Neural Networks},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - NCTA},
year={2020},
pages={479-484},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010177204790484},
isbn={978-989-758-475-6},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - NCTA
TI - Generative Modeling of Synthetic Eye-tracking Data: NLP-based Approach with Recurrent Neural Networks
SN - 978-989-758-475-6
IS - 2184-3236
AU - Elbattah, M.
AU - Guérin, J.
AU - Carette, R.
AU - Cilia, F.
AU - Dequen, G.
PY - 2020
SP - 479
EP - 484
DO - 10.5220/0010177204790484
PB - SciTePress

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