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.…
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…