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Riemannian Classification and Regression for EEGEyeNet

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HCI International 2022 – Late Breaking Posters (HCII 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1654))

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Abstract

In this paper, we use emerging Riemannian geometry based classifiers and regressors to perform eye-tracking tasks over a 2021 dataset: EEGEyeNet. The classification task we attempt is determining Left/Right eye movement, and the regression task we attempt is determining absolute eye position on a Cartesian plane. We find that Riemannian methods are not more accurate than traditional ML techniques, and offer suggestions for future improvement.

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Correspondence to Derrick Zhen .

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Zhen, D., Berreby, G. (2022). Riemannian Classification and Regression for EEGEyeNet. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2022 – Late Breaking Posters. HCII 2022. Communications in Computer and Information Science, vol 1654. Springer, Cham. https://doi.org/10.1007/978-3-031-19679-9_15

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  • DOI: https://doi.org/10.1007/978-3-031-19679-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19678-2

  • Online ISBN: 978-3-031-19679-9

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