Evaluation of preference of multimedia content using deep neural networks for electroencephalography

Seong Eun Moon, Soobeom Jang, Jong Seok Lee

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

Abstract

Evaluation of quality of experience (Qo $E$) based on electroencephalography (EEG) has received great attention due to its capability of real-time Qo $E$ monitoring of users. However, it still suffers from rather low recognition accuracy. In this paper, we propose a novel method using deep neural networks toward improved modeling of EEG and thereby improved recognition accuracy. In particular, we aim to model spatio-temporal characteristics relevant for QoE analysis within learning models. The results demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2018 10th International Conference on Quality of Multimedia Experience, QoMEX 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538626054
DOIs
Publication statusPublished - 2018 Sep 11
Event10th International Conference on Quality of Multimedia Experience, QoMEX 2018 - Sardinia, Italy
Duration: 2018 May 292018 Jun 1

Publication series

Name2018 10th International Conference on Quality of Multimedia Experience, QoMEX 2018

Other

Other10th International Conference on Quality of Multimedia Experience, QoMEX 2018
CountryItaly
CitySardinia
Period18/5/2918/6/1

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Moon, S. E., Jang, S., & Lee, J. S. (2018). Evaluation of preference of multimedia content using deep neural networks for electroencephalography. In 2018 10th International Conference on Quality of Multimedia Experience, QoMEX 2018 [8463373] (2018 10th International Conference on Quality of Multimedia Experience, QoMEX 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/QoMEX.2018.8463373