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.
|Title of host publication||2018 10th International Conference on Quality of Multimedia Experience, QoMEX 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2018 Sep 11|
|Event||10th International Conference on Quality of Multimedia Experience, QoMEX 2018 - Sardinia, Italy|
Duration: 2018 May 29 → 2018 Jun 1
|Name||2018 10th International Conference on Quality of Multimedia Experience, QoMEX 2018|
|Other||10th International Conference on Quality of Multimedia Experience, QoMEX 2018|
|Period||18/5/29 → 18/6/1|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (NRF-2016R1E1A1A01943283).
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Media Technology
- Safety, Risk, Reliability and Quality