6 citations · 24 across the 27 of their papers we have counts for
27 papers
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…
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…
DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework
Ji-Hoon Jeong, In-Gyu Lee, Sung-Kyung Kim +3
Childhood and adolescent obesity rates are a global concern because obesity is associated with chronic diseases and long-term health risks. Artificial intelligence technology has e…
WUDI: A Human Involved Self-Adaptive Framework to Prevent Childhood Obesity in Internet of Things Environment
Euijong Lee, Jaemin Jung, Gee-Myung Moon +2
The Internet of Things (IoT) connects people, devices, and information resources, in various domains to improve efficiency. The healthcare domain has been transformed by the integr…
Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces
Serkan Musellim, Dong-Kyun Han, Ji-Hoon Jeong +1
Brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitate…
Decoding Visual Imagery from EEG Signals using Visual Perception Guided Network Training Method
Byoung-Hee Kwon, Jeong-Hyun Cho, Byeong-Hoo Lee
An electroencephalogram is an effective approach that provides a bidirectional pathway between user and computer in a non-invasive way. In this study, we adopted the visual percept…
A dynamic intermediate state limits the folding rate of a discontinuous two-domain protein
Ganesh Agam, Anders Barth, Don C. Lamb
Protein folding is an indispensable process for the majority of proteins after their synthesis from ribosomes in the cell. Most in vitro protein folding studies have focused on sin…
Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks
Hyung-Ju Ahn, Dae-Hyeok Lee, Ji-Hoon Jeong +1
Human-robot collaboration has the potential to maximize the efficiency of the operation of autonomous robots. Brain-machine interface (BMI) would be a desirable technology to colla…
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…
Visual Motion Imagery Classification with Deep Neural Network based on Functional Connectivity
Byoung-Hee Kwon, Ji-Hoon Jeong, Seong-Whan Lee
Brain-computer interfaces (BCIs) use brain signals such as electroencephalography to reflect user intention and enable two-way communication between computers and users. BCI techno…
Towards Neurohaptics: Brain-Computer Interfaces for Decoding Intuitive Sense of Touch
Jeong-Hyun Cho, Ji-Hoon Jeong, Myoung-Ki Kim +1
Noninvasive brain-computer interface (BCI) is widely used to recognize users' intentions. Especially, BCI related to tactile and sensation decoding could provide various effects on…
Design of an EEG-based Drone Swarm Control System using Endogenous BCI Paradigms
Dae-Hyeok Lee, Hyung-Ju Ahn, Ji-Hoon Jeong +1
Non-invasive brain-computer interface (BCI) has been developed for understanding users' intentions by using electroencephalogram (EEG) signals. With the recent development of artif…
Classification of Tactile Perception and Attention on Natural Textures from EEG Signals
Myoung-Ki Kim, Jeong-Hyun Cho, Ji-Hoon Jeong
Brain-computer interface allows people who have lost their motor skills to control robot limbs based on electroencephalography. Most BCIs are guided only by visual feedback and do…
Speech Imagery Classification using Length-Wise Training based on Deep Learning
Byeong-Hoo Lee, Byeong-Hee Kwon, Do-Yeun Lee +1
Brain-computer interface uses brain signals to control external devices without actual control behavior. Recently, speech imagery has been studied for direct communication using la…
Motor Imagery Classification Emphasizing Corresponding Frequency Domain Method based on Deep Learning Framework
Byoung-Hee Kwon, Byeong-Hoo Lee, Ji-Hoon Jeong
The electroencephalogram, a type of non-invasive-based brain signal that has a user intention-related feature provides an efficient bidirectional pathway between user and computer.…
Domain Generalization for Session-Independent Brain-Computer Interface
Dong-Kyun Han, Ji-Hoon Jeong
The inter/intra-subject variability of electroencephalography (EEG) makes the practical use of the brain-computer interface (BCI) difficult. In general, the BCI system requires a c…
Decoding of Intuitive Visual Motion Imagery Using Convolutional Neural Network under 3D-BCI Training Environment
Byoung-Hee Kwon, Ji-Hoon Jeong, Jeong-Hyun Cho +1
In this study, we adopted visual motion imagery, which is a more intuitive brain-computer interface (BCI) paradigm, for decoding the intuitive user intention. We developed a 3-dime…
Decoding of Grasp Motions from EEG Signals Based on a Novel Data Augmentation Strategy
Jeong-Hyun Cho, Ji-Hoon Jeong, Seong-Whan Lee
Electroencephalogram (EEG) based brain-computer interface (BCI) systems are useful tools for clinical purposes like neural prostheses. In this study, we collected EEG signals relat…
Gradual Relation Network: Decoding Intuitive Upper Extremity Movement Imaginations Based on Few-Shot EEG Learning
Kyung-Hwan Shim, Ji-Hoon Jeong, Seong-Whan Lee
Brain-computer interface (BCI) is a communication tool that connects users and external devices. In a real-time BCI environment, a calibration procedure is particularly necessary f…
Classification of Visual Perception and Imagery based EEG Signals Using Convolutional Neural Networks
Ji-Seon Bang, Ji-Hoon Jeong, Dong-Ok Won
Recently, visual perception (VP) and visual imagery (VI) paradigms are investigated in several brain-computer interface (BCI) studies. VP and VI are defined as a changing of brain…
Motor Imagery Classification of Single-Arm Tasks Using Convolutional Neural Network based on Feature Refining
Byeong-Hoo Lee, Ji-Hoon Jeong, Kyung-Hwan Shim +1
Brain-computer interface (BCI) decodes brain signals to understand user intention and status. Because of its simple and safe data acquisition process, electroencephalogram (EEG) is…
Classification of Upper Limb Movements \newline Using Convolutional Neural Network \newline with 3D Inception Block
D. -Y. Lee, J. -H. Jeong, K. -H. Shim +1
A brain-machine interface (BMI) based on electroencephalography (EEG) can overcome the movement deficits for patients and real-world applications for healthy people. Ideally, the B…
A Novel Framework for Visual Motion Imagery Classification Using 3D Virtual BCI Platform
Byoung-Hee Kwon, Ji-Hoon Jeong, Dong-Joo Kim
In this study, 3D brain-computer interface (BCI) training platforms were used to stimulate the subjects for visual motion imagery and visual perception. We measured the activation…
Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural network
Byeong-Hoo Lee, Ji-Hoon Jeong, Kyung-Hwan Shim +1
A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. Electroencephalogram (EEG) motor imagery (MI) paradigm is widely used in…