On the Repeatability of EEG-Based Image Quality Assessment

Jaehui Hwang, Seong Eun Moon, Jong-Seok Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Electroencephalography (EEG) has attracted much attention because it allows to monitor user states in real time and is applicable to applications for improvement of multimedia user experience. The repeatability of EEG-based perceptual response analysis is critical for the reliability of such applications, which has not been sufficiently addressed in previous studies. In this paper, we evaluate the repeatability of EEG-based image quality assessment. We repeatedly perform the same experiment for three successive days. Then, we design a classification system that discriminates the quality of images based on EEG, whose performance is examined across the data collected in different days. We reveal that the repeatability of image quality evaluation using EEG decreases as time goes and the memory effect intensifies the loss of repeatability.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1785-1788
Number of pages4
ISBN (Electronic)9781538666500
DOIs
Publication statusPublished - 2019 Jan 16
Event2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan
Duration: 2018 Oct 72018 Oct 10

Publication series

NameProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
CountryJapan
CityMiyazaki
Period18/10/718/10/10

Fingerprint

Electroencephalography
Image quality
Multimedia
Quality assessment
Data storage equipment
Experiments

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Information Systems and Management
  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Human-Computer Interaction

Cite this

Hwang, J., Moon, S. E., & Lee, J-S. (2019). On the Repeatability of EEG-Based Image Quality Assessment. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 1785-1788). [8616304] (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2018.00308
Hwang, Jaehui ; Moon, Seong Eun ; Lee, Jong-Seok. / On the Repeatability of EEG-Based Image Quality Assessment. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1785-1788 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).
@inproceedings{01f6772471d94c12857f58bbee7a4647,
title = "On the Repeatability of EEG-Based Image Quality Assessment",
abstract = "Electroencephalography (EEG) has attracted much attention because it allows to monitor user states in real time and is applicable to applications for improvement of multimedia user experience. The repeatability of EEG-based perceptual response analysis is critical for the reliability of such applications, which has not been sufficiently addressed in previous studies. In this paper, we evaluate the repeatability of EEG-based image quality assessment. We repeatedly perform the same experiment for three successive days. Then, we design a classification system that discriminates the quality of images based on EEG, whose performance is examined across the data collected in different days. We reveal that the repeatability of image quality evaluation using EEG decreases as time goes and the memory effect intensifies the loss of repeatability.",
author = "Jaehui Hwang and Moon, {Seong Eun} and Jong-Seok Lee",
year = "2019",
month = "1",
day = "16",
doi = "10.1109/SMC.2018.00308",
language = "English",
series = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1785--1788",
booktitle = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",
address = "United States",

}

Hwang, J, Moon, SE & Lee, J-S 2019, On the Repeatability of EEG-Based Image Quality Assessment. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616304, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1785-1788, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 18/10/7. https://doi.org/10.1109/SMC.2018.00308

On the Repeatability of EEG-Based Image Quality Assessment. / Hwang, Jaehui; Moon, Seong Eun; Lee, Jong-Seok.

Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1785-1788 8616304 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - On the Repeatability of EEG-Based Image Quality Assessment

AU - Hwang, Jaehui

AU - Moon, Seong Eun

AU - Lee, Jong-Seok

PY - 2019/1/16

Y1 - 2019/1/16

N2 - Electroencephalography (EEG) has attracted much attention because it allows to monitor user states in real time and is applicable to applications for improvement of multimedia user experience. The repeatability of EEG-based perceptual response analysis is critical for the reliability of such applications, which has not been sufficiently addressed in previous studies. In this paper, we evaluate the repeatability of EEG-based image quality assessment. We repeatedly perform the same experiment for three successive days. Then, we design a classification system that discriminates the quality of images based on EEG, whose performance is examined across the data collected in different days. We reveal that the repeatability of image quality evaluation using EEG decreases as time goes and the memory effect intensifies the loss of repeatability.

AB - Electroencephalography (EEG) has attracted much attention because it allows to monitor user states in real time and is applicable to applications for improvement of multimedia user experience. The repeatability of EEG-based perceptual response analysis is critical for the reliability of such applications, which has not been sufficiently addressed in previous studies. In this paper, we evaluate the repeatability of EEG-based image quality assessment. We repeatedly perform the same experiment for three successive days. Then, we design a classification system that discriminates the quality of images based on EEG, whose performance is examined across the data collected in different days. We reveal that the repeatability of image quality evaluation using EEG decreases as time goes and the memory effect intensifies the loss of repeatability.

UR - http://www.scopus.com/inward/record.url?scp=85062210142&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062210142&partnerID=8YFLogxK

U2 - 10.1109/SMC.2018.00308

DO - 10.1109/SMC.2018.00308

M3 - Conference contribution

AN - SCOPUS:85062210142

T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

SP - 1785

EP - 1788

BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Hwang J, Moon SE, Lee J-S. On the Repeatability of EEG-Based Image Quality Assessment. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1785-1788. 8616304. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). https://doi.org/10.1109/SMC.2018.00308