1 citations · 3 across the 14 of their papers we have counts for
14 papers
Hybrid Paradigm-based Brain-Computer Interface for Robotic Arm Control
Byeong-Hoo Lee, Jeong-Hyun Cho, Byung-Hee Kwon
Brain-computer interface (BCI) uses brain signals to communicate with external devices without actual control. Particularly, BCI is one of the interfaces for controlling the roboti…
Decoding Multi-class Motor-related Intentions with User-optimized and Robust BCI System Based on Multimodal Dataset
Jeong-Hyun Cho, Byoung-Hee Kwon, Byeong-Hoo Lee
A brain-computer interface (BCI) based on electroencephalography (EEG) can be useful for rehabilitation and the control of external devices. Five grasping tasks were decoded for mo…
Target-centered Subject Transfer Framework for EEG Data Augmentation
Kang Yin, Byeong-Hoo Lee, Byoung-Hee Kwon +1
Data augmentation approaches are widely explored for the enhancement of decoding electroencephalogram signals. In subject-independent brain-computer interface system, domain adapti…
Channel Optimized Visual Imagery based Robotic Arm Control under the Online Environment
Byoung-Hee Kwon, Byeong-Hoo Lee, Jeong-Hyun Cho
An electroencephalogram is an effective approach that provides a bidirectional pathway between the user and computer in a non-invasive way. In this study, we adopted the visual ima…
Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface
Byeong-Hoo Lee, Jeong-Hyun Cho, Byoung-Hee Kwon +1
Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performan…
Decoding Continual Muscle Movements Related to Complex Hand Grasping from EEG Signals
Jeong-Hyun Cho, Byoung-Hee Kwon, Byeong-Hoo Lee +1
Brain-computer interface (BCI) is a practical pathway to interpret users' intentions by decoding motor execution (ME) or motor imagery (MI) from electroencephalogram (EEG) signals.…
Recognition of Tactile-related EEG Signals Generated by Self-touch
Myoung-Ki Kim, Jeong-Hyun Cho, Hye-Bin Shin
Touch is the first sense among human senses. Not only that, but it is also one of the most important senses that are indispensable. However, compared to sight and hearing, it is of…
A Factorization Approach for Motor Imagery Classification
Byeong-Hoo Lee, Jeong-Hyun Cho, Byung-Hee Kwon
Brain-computer interface uses brain signals to communicate with external devices without actual control. Many studies have been conducted to classify motor imagery based on machine…
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 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…
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…
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…
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…