5 citations · 9 across the 14 of their papers we have counts for
14 papers
Modularity-Free Conflict-Averse Training for Generalized PINNs
Heejo Kong, Beomchul Park, Sung-Jin Kim +1
Physics-informed neural networks (PINNs) have become a powerful framework for solving PDEs by embedding physical laws into differentiable objectives. Despite their advances, traini…
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers
Heejo Kong, Sung-Jin Kim, Gunho Jung +1
Conventional semi-supervised learning (SSL) ideally assumes that labeled and unlabeled data share an identical class distribution, however in practice, this assumption is easily vi…
Dataset Refinement for Improving the Generalization Ability of the EEG Decoding Model
Sung-Jin Kim, Dae-Hyeok Lee, Hyeon-Taek Han
Electroencephalography (EEG) is a generally used neuroimaging approach in brain-computer interfaces due to its non-invasive characteristics and convenience, making it an effective…
Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination
Si-Hyun Kim, Sung-Jin Kim, Dae-Hyeok Lee
Brain-computer interface (BCI) enables direct communication between the brain and external devices by decoding neural signals, offering potential solutions for individuals with mot…
Decoding Fatigue Levels of Pilots Using EEG Signals with Hybrid Deep Neural Networks
Dae-Hyeok Lee, Sung-Jin Kim, Si-Hyun Kim
The detection of pilots' mental states is critical, as abnormal mental states have the potential to cause catastrophic accidents. This study demonstrates the feasibility of using d…
A Distributed Inference System for Detecting Task-wise Single Trial Event-Related Potential in Stream of Satellite Images
Sung-Jin Kim, Heon-Gyu Kwak, Hyeon-Taek Han +3
Brain-computer interface (BCI) has garnered the significant attention for their potential in various applications, with event-related potential (ERP) performing a considerable role…
Decoding EEG-based Workload Levels Using Spatio-temporal Features Under Flight Environment
Dae-Hyeok Lee, Sung-Jin Kim, Si-Hyun Kim +1
The detection of pilots' mental states is important due to the potential for their abnormal mental states to result in catastrophic accidents. This study introduces the feasibility…
Influence of Video Dynamics on EEG-based Single-Trial Video Target Surveillance System
Heon-Gyu Kwak, Sung-Jin Kim, Hyeon-Taek Han +2
Target detection models are one of the widely used deep learning-based applications for reducing human efforts on video surveillance and patrol. However, the application of convent…
Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals
Dae-Hyeok Lee, Sung-Jin Kim, Yeon-Woo Choi
Non-invasive brain-computer interface technology has been developed for detecting human mental states with high performances. Detection of the pilots' mental states is particularly…
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals
Sung-Jin Kim, Dae-Hyeok Lee, Yeon-Woo Choi
Brain-computer interface (BCI) is a communication system between humans and computers reflecting human intention without using a physical control device. Since deep learning is rob…
Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals
Keon-Woo Lee, Dae-Hyeok Lee, Sung-Jin Kim +1
Speech impairments due to cerebral lesions and degenerative disorders can be devastating. For humans with severe speech deficits, imagined speech in the brain-computer interface ha…
Decoding High-level Imagined Speech using Attention-based Deep Neural Networks
Dae-Hyeok Lee, Sung-Jin Kim, Keon-Woo Lee
Brain-computer interface (BCI) is the technology that enables the communication between humans and devices by reflecting status and intentions of humans. When conducting imagined s…
DAL: Feature Learning from Overt Speech to Decode Imagined Speech-based EEG Signals with Convolutional Autoencoder
Dae-Hyeok Lee, Sung-Jin Kim, Seong-Whan Lee
Brain-computer interface (BCI) is one of the tools which enables the communication between humans and devices by reflecting intention and status of humans. With the development of…
Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks
Dae-Hyeok Lee, Dong-Kyun Han, Sung-Jin Kim +2
Brain-computer interface (BCI) is used for communication between humans and devices by recognizing status and intention of humans. Communication between humans and a drone using el…