94 citations · 177 across the 8 of their papers we have counts for
8 papers
Development of deep biological ages aware of morbidity and mortality based on unsupervised and semi-supervised deep learning approaches
Seong-Eun Moon, Ji Won Yoon, Shinyoung Joo +5
Background: While deep learning technology, which has the capability of obtaining latent representations based on large-scale data, can be a potential solution for the discovery of…
Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks
Seong-Eun Moon, Chun-Jui Chen, Cho-Jui Hsieh +2
Convolutional neural networks (CNNs) are widely used to recognize the user's state through electroencephalography (EEG) signals. In the previous studies, the EEG signals are usuall…
EEG-based Emotional Video Classification via Learning Connectivity Structure
Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee
Electroencephalography (EEG) is a useful way to implicitly monitor the users perceptual state during multimedia consumption. One of the primary challenges for the practical use of…
Perceptual Experience Analysis for Tone-mapped HDR Videos based on EEG and Peripheral Physiological Signals
Seong-Eun Moon, Jong-Seok Lee
High dynamic range (HDR) imaging has been attracting much attention as a technology that can provide immersive experience. Its ultimate goal is to provide better quality of experie…
Implicit Analysis of Perceptual Multimedia Experience Based on Physiological Response: A Review
Seong-Eun Moon, Jong-Seok Lee
The exponential growth of popularity of multimedia has led to needs for user-centric adaptive applications that manage multimedia content more effectively. Implicit analysis, which…
EEG-based video identification using graph signal modeling and graph convolutional neural network
Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee
This paper proposes a novel graph signal-based deep learning method for electroencephalography (EEG) and its application to EEG-based video identification. We present new methods t…