Evaluation of Preference of Multimedia Content using Deep Neural Networks for Electroencephalography

TL;DR

This paper introduces a deep neural network approach to improve EEG-based quality of experience evaluation for multimedia content, focusing on modeling spatio-temporal features to enhance recognition accuracy.

Contribution

The paper presents a novel deep learning method that better captures EEG spatio-temporal features for improved QoE recognition accuracy.

Findings

Demonstrated improved EEG recognition accuracy with the proposed method.

Effectively modeled spatio-temporal EEG features for QoE analysis.

Validated the approach's effectiveness through experimental results.

Abstract

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.