2 citations · 3 across the 5 of their papers we have counts for
5 papers · 1 filter
NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
Cheol-Hui Lee, Hakseung Kim, Hyun-jee Han +3
The classification of sleep stages is a pivotal aspect of diagnosing sleep disorders and evaluating sleep quality. However, the conventional manual scoring process, conducted by cl…
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…