Evaluation of Preference of Multimedia Content using Deep Neural Networks for Electroencephalography
This work addresses the need for better real-time QoE monitoring in multimedia content evaluation, though it appears incremental as it builds on existing EEG-based methods.
The paper tackled the problem of low recognition accuracy in quality of experience (QoE) evaluation using electroencephalography (EEG) by proposing a novel deep neural network method to model spatio-temporal characteristics, resulting in improved recognition accuracy.
Evaluation of quality of experience (QoE) based on electroencephalography (EEG) has received great attention due to its capability of real-time QoE 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.