6 citations · 24 across the 27 of their papers we have counts for
15 papers · 1 filter
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…
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…
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…
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 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…
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…
A novel approach to classify natural grasp actions by estimating muscle activity patterns from EEG signals
Jeong-Hyun Cho, Ji-Hoon Jeong, Dong-Joo Kim +1
Developing electroencephalogram (EEG) based brain-computer interface (BCI) systems is challenging. In this study, we analyzed natural grasp actions from EEG. Ten healthy subjects p…