6 papers
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…
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.…
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…
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…