2 citations · 2 across the 16 of their papers we have counts for
5 papers · 1 filter
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…