14 papers
Towards a Network Expansion Approach for Reliable Brain-Computer Interface
Byeong-Hoo Lee, Kang Yin
Robotic arms are increasingly being used in collaborative environments, requiring an accurate understanding of human intentions to ensure both effectiveness and safety. Electroence…
Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface
Byeong-Hoo Lee, Byoung-Hee Kwon, Seong-Whan Lee
Deep learning has shown promise in decoding brain signals, such as electroencephalogram (EEG), in the field of brain-computer interfaces (BCIs). However, the non-stationary charact…
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.…
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…
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.…
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 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…