short-paper

Characterization and reconstruction of VOG noise with power spectral density analysis

Authors:

Dong Wang,

Jeff B. Pelz,

Fiona MulveyAuthors Info & Claims

ETRA '16: Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications

Pages 217 - 220

https://doi.org/10.1145/2857491.2857516

Published: 14 March 2016 Publication History

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Abstract

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 Kämpfer 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.

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Cited By

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Dai WSelesnick IRizzo JRucker JHudson T(2021)Detection of normal and slow saccades using implicit piecewise polynomial approximationJournal of Vision10.1167/jov.21.6.821:6(8)Online publication date: 14-Jun-2021
https://doi.org/10.1167/jov.21.6.8
Zemblys RNiehorster DKomogortsev OHolmqvist K(2017)Using machine learning to detect events in eye-tracking dataBehavior Research Methods10.3758/s13428-017-0860-350:1(160-181)Online publication date: 23-Feb-2017
https://doi.org/10.3758/s13428-017-0860-3

Index Terms

Characterization and reconstruction of VOG noise with power spectral density analysis
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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Published In

ETRA '16: Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications

March 2016

378 pages

ISBN:9781450341257

DOI:10.1145/2857491

Conference Chairs:
Pernilla Qvarfordt
FX Palo Alto Laboratory
,
Dan Witzner Hansen
IT University of Copenhagen, Denmark

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 March 2016

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ETRA '16: 2016 Symposium on Eye Tracking Research and Applications

March 14 - 17, 2016

South Carolina, Charleston

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Dai WSelesnick IRizzo JRucker JHudson T(2021)Detection of normal and slow saccades using implicit piecewise polynomial approximationJournal of Vision10.1167/jov.21.6.821:6(8)Online publication date: 14-Jun-2021
https://doi.org/10.1167/jov.21.6.8
Zemblys RNiehorster DKomogortsev OHolmqvist K(2017)Using machine learning to detect events in eye-tracking dataBehavior Research Methods10.3758/s13428-017-0860-350:1(160-181)Online publication date: 23-Feb-2017
https://doi.org/10.3758/s13428-017-0860-3

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Measuring Focused Attention Using Fixation Inner-Density

Comparative study of several Fir median hybrid filters for blink noise removal in Electrooculograms

Analysing EOG signal features for the discrimination of eye movements with wearable devices