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Characterization and reconstruction of VOG noise with power spectral density analysis

Wang, Dong; Pelz, Jeff B. and Mulvey, Fiona LU (2016) 9th Biennial ACM Symposium on Eye Tracking Research and Applications, ETRA 2016 In Proceedings - ETRA 2016: 2016 ACM Symposium on Eye Tracking Research and Applications 14. p.217-220
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 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... (More)

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.

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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Eye tracking, Noise modeling, Power spectral analysis
in
Proceedings - ETRA 2016: 2016 ACM Symposium on Eye Tracking Research and Applications
volume
14
pages
4 pages
publisher
Association for Computing Machinery (ACM)
conference name
9th Biennial ACM Symposium on Eye Tracking Research and Applications, ETRA 2016
external identifiers
  • scopus:84975282607
DOI
10.1145/2857491.2857516
language
English
LU publication?
yes
id
5cc6c6c7-0301-40ea-ae1d-8b400cbeffde
date added to LUP
2016-10-07 14:28:30
date last changed
2017-01-01 08:36:14
@inproceedings{5cc6c6c7-0301-40ea-ae1d-8b400cbeffde,
  abstract     = {<p>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.</p>},
  author       = {Wang, Dong and Pelz, Jeff B. and Mulvey, Fiona},
  booktitle    = {Proceedings - ETRA 2016: 2016 ACM Symposium on Eye Tracking Research and Applications},
  keyword      = {Eye tracking,Noise modeling,Power spectral analysis},
  language     = {eng},
  month        = {03},
  pages        = {217--220},
  publisher    = {Association for Computing Machinery (ACM)},
  title        = {Characterization and reconstruction of VOG noise with power spectral density analysis},
  url          = {http://dx.doi.org/10.1145/2857491.2857516},
  volume       = {14},
  year         = {2016},
}