4 papers
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…
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…