4 citations · 8 across the 14 of their papers we have counts for
14 papers
Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination
Si-Hyun Kim, Sung-Jin Kim, Dae-Hyeok Lee
Brain-computer interface (BCI) enables direct communication between the brain and external devices by decoding neural signals, offering potential solutions for individuals with mot…
Decoding Fatigue Levels of Pilots Using EEG Signals with Hybrid Deep Neural Networks
Dae-Hyeok Lee, Sung-Jin Kim, Si-Hyun Kim
The detection of pilots' mental states is critical, as abnormal mental states have the potential to cause catastrophic accidents. This study demonstrates the feasibility of using d…
A Distributed Inference System for Detecting Task-wise Single Trial Event-Related Potential in Stream of Satellite Images
Sung-Jin Kim, Heon-Gyu Kwak, Hyeon-Taek Han +3
Brain-computer interface (BCI) has garnered the significant attention for their potential in various applications, with event-related potential (ERP) performing a considerable role…
Decoding EEG-based Workload Levels Using Spatio-temporal Features Under Flight Environment
Dae-Hyeok Lee, Sung-Jin Kim, Si-Hyun Kim +1
The detection of pilots' mental states is important due to the potential for their abnormal mental states to result in catastrophic accidents. This study introduces the feasibility…
Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals
Dae-Hyeok Lee, Sung-Jin Kim, Yeon-Woo Choi
Non-invasive brain-computer interface technology has been developed for detecting human mental states with high performances. Detection of the pilots' mental states is particularly…
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals
Sung-Jin Kim, Dae-Hyeok Lee, Yeon-Woo Choi
Brain-computer interface (BCI) is a communication system between humans and computers reflecting human intention without using a physical control device. Since deep learning is rob…
Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals
Keon-Woo Lee, Dae-Hyeok Lee, Sung-Jin Kim +1
Speech impairments due to cerebral lesions and degenerative disorders can be devastating. For humans with severe speech deficits, imagined speech in the brain-computer interface ha…
Decoding 3D Representation of Visual Imagery EEG using Attention-based Dual-Stream Convolutional Neural Network
Hyung-Ju Ahn, Dae-Hyeok Lee
A deep neural network has been successfully applied to an electroencephalogram (EEG)-based brain-computer interface. However, in most studies, the correlation between EEG channels…
Decoding High-level Imagined Speech using Attention-based Deep Neural Networks
Dae-Hyeok Lee, Sung-Jin Kim, Keon-Woo Lee
Brain-computer interface (BCI) is the technology that enables the communication between humans and devices by reflecting status and intentions of humans. When conducting imagined s…
DAL: Feature Learning from Overt Speech to Decode Imagined Speech-based EEG Signals with Convolutional Autoencoder
Dae-Hyeok Lee, Sung-Jin Kim, Seong-Whan Lee
Brain-computer interface (BCI) is one of the tools which enables the communication between humans and devices by reflecting intention and status of humans. With the development of…
Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks
Hyung-Ju Ahn, Dae-Hyeok Lee, Ji-Hoon Jeong +1
Human-robot collaboration has the potential to maximize the efficiency of the operation of autonomous robots. Brain-machine interface (BMI) would be a desirable technology to colla…
Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks
Dae-Hyeok Lee, Dong-Kyun Han, Sung-Jin Kim +2
Brain-computer interface (BCI) is used for communication between humans and devices by recognizing status and intention of humans. Communication between humans and a drone using el…
Design of an EEG-based Drone Swarm Control System using Endogenous BCI Paradigms
Dae-Hyeok Lee, Hyung-Ju Ahn, Ji-Hoon Jeong +1
Non-invasive brain-computer interface (BCI) has been developed for understanding users' intentions by using electroencephalogram (EEG) signals. With the recent development of artif…
Towards Brain-Computer Interfaces for Drone Swarm Control
Ji-Hoon Jeong, Dae-Hyeok Lee, Hyung-Ju Ahn +1
Noninvasive brain-computer interface (BCI) decodes brain signals to understand user intention. Recent advances have been developed for the BCI-based drone control system as the dem…