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Characterization and reconstruction of VOG noise with power spectral density analysis

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Author

  • Dong Wang
  • Jeff B. Pelz
  • Fiona Mulvey

Summary, in English

Characterizing noise in eye movement data is important for data analysis, as well as for the comparison of research results across systems. We present a method that characterizes and reconstructs the noise in eye movement data from video-oculography (VOG) systems taking into account the uneven sampling in real recordings due to track loss and inherent system features. The proposed method extends the Lomb-Scargle periodogram, which is used for the estimation of the power spectral density (PSD) of unevenly sampled data [Hocke and Kampfer 2009]. We estimate the PSD of fixational eye movement data and reconstruct the noise by applying a random phase to the inverse Fourier transform so that the reconstructed signal retains the amplitude of the original noise at each frequency. We apply this method to the EMRA/COGAIN Eye Data Quality Standardization project's dataset, which includes recordings from 11 commercially available VOG systems and a Dual Pukinje Image (DPI) eye tracker. The reconstructed noise from each VOG system was superimposed onto the DPI data and the resulting eye movement measures from the same original behaviors were compared.

Publishing year

2016-03-14

Language

English

Pages

217-220

Publication/Series

Proceedings - ETRA 2016: 2016 ACM Symposium on Eye Tracking Research and Applications

Volume

14

Document type

Conference paper

Publisher

Association for Computing Machinery (ACM)

Topic

  • Other Humanities not elsewhere specified
  • Computer and Information Science

Keywords

  • Eye tracking
  • Noise modeling
  • Power spectral analysis

Conference name

9th Biennial ACM Symposium on Eye Tracking Research and Applications, ETRA 2016

Conference date

2016-03-14 - 2016-03-17

Conference place

Charleston, United States

Status

Published

Project

  • Eye Data Quality Standardisation

ISBN/ISSN/Other

  • ISBN: 9781450341257