Characterization and reconstruction of VOG noise with power spectral density analysis
(2016) 9th Biennial ACM Symposium on Eye Tracking Research and Applications, ETRA 2016 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
- Wang, Dong ; Pelz, Jeff B. and Mulvey, Fiona LU
- organization
- publishing date
- 2016-03-14
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Eye tracking, Noise modeling, Power spectral analysis
- host publication
- 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
- conference location
- Charleston, United States
- conference dates
- 2016-03-14 - 2016-03-17
- external identifiers
-
- scopus:84975282607
- wos:000389809700032
- ISBN
- 9781450341257
- DOI
- 10.1145/2857491.2857516
- project
- Eye Data Quality Standardisation
- language
- English
- LU publication?
- yes
- id
- 5cc6c6c7-0301-40ea-ae1d-8b400cbeffde
- date added to LUP
- 2016-10-07 14:28:30
- date last changed
- 2024-03-07 13:35:38
@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}},
isbn = {{9781450341257}},
keywords = {{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}},
doi = {{10.1145/2857491.2857516}},
volume = {{14}},
year = {{2016}},
}