7 citations · 12 across the 16 of their papers we have counts for
18 papers
Local-Global Temporal Fusion Network with an Attention Mechanism for Multiple and Multiclass Arrhythmia Classification
Yun Kwan Kim, Minji Lee, Kunwook Jo +2
Clinical decision support systems (CDSSs) have been widely utilized to support the decisions made by cardiologists when detecting and classifying arrhythmia from electrocardiograms…
Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running
Young-Eun Lee, Gi-Hwan Shin, Minji Lee +1
We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four differen…
Functional Connectivity of Imagined Speech and Visual Imagery based on Spectral Dynamics
Seo-Hyun Lee, Minji Lee, Seong-Whan Lee
Recent advances in brain-computer interface technology have shown the potential of imagined speech and visual imagery as a robust paradigm for intuitive brain-computer interface co…
Automatic Micro-sleep Detection under Car-driving Simulation Environment using Night-sleep EEG
Young-Seok Kweon, Gi-Hwan Shin, Heon-Gyu Kwak +1
A micro-sleep is a short sleep that lasts from 1 to 30 secs. Its detection during driving is crucial to prevent accidents that could claim a lot of people's lives. Electroencephalo…
Predicting the Transition from Short-term to Long-term Memory based on Deep Neural Network
Gi-Hwan Shin, Young-Seok Kweon, Minji Lee
Memory is an essential element in people's daily life based on experience. So far, many studies have analyzed electroencephalogram (EEG) signals at encoding to predict later rememb…
Decoding Visual Recognition of Objects from EEG Signals based on Attention-Driven Convolutional Neural Network
Jenifer Kalafatovich, Minji Lee, Seong-Whan Lee
The ability to perceive and recognize objects is fundamental for the interaction with the external environment. Studies that investigate them and their relationship with brain acti…
Classification of Imagined Speech Using Siamese Neural Network
Dong-Yeon Lee, Minji Lee, Seong-Whan Lee
Imagined speech is spotlighted as a new trend in the brain-machine interface due to its application as an intuitive communication tool. However, previous studies have shown low cla…
Reconstructing ERP Signals Using Generative Adversarial Networks for Mobile Brain-Machine Interface
Young-Eun Lee, Minji Lee, Seong-Whan Lee
Practical brain-machine interfaces have been widely studied to accurately detect human intentions using brain signals in the real world. However, the electroencephalography (EEG) s…
Assessment of Unconsciousness for Memory Consolidation Using EEG Signals
Gi-Hwan Shin, Minji Lee, Seong-Whan Lee
The assessment of consciousness and unconsciousness is a challenging issue in modern neuroscience. Consciousness is closely related to memory consolidation in that memory is a crit…
Prediction of Memory Retrieval Performance Using Ear-EEG Signals
Jenifer Kalafatovich, Minji Lee, Seong-Whan Lee
Many studies have explored brain signals during the performance of a memory task to predict later remembered items. However, prediction methods are still poorly used in real life a…
Prediction of Event Related Potential Speller Performance Using Resting-State EEG
Gi-Hwan Shin, Minji Lee, Hyeong-Jin Kim +1
Event-related potential (ERP) speller can be utilized in device control and communication for locked-in or severely injured patients. However, problems such as inter-subject perfor…
End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features
Hyeong-Jin Kim, Minji Lee, Seong-Whan Lee
Sleep disorder is one of many neurological diseases that can affect greatly the quality of daily life. It is very burdensome to manually classify the sleep stages to detect sleep d…
Spatio-Temporal Dynamics of Visual Imagery for Intuitive Brain-Computer Interface
Seo-Hyun Lee, Minji Lee, Seong-Whan Lee
Visual imagery is an intuitive brain-computer interface paradigm, referring to the emergence of the visual scene. Despite its convenience, analysis of its intrinsic characteristics…
Prediction of Individual Propofol Requirements based on Preoperative EEG Signals
Young-Seok Kweon, Minji Lee, Dong-Ok Won +1
The patient must be given an adequate amount of propofol for safe surgery since overcapacity and low capacity cause accidents. However, the sensitivity of propofol varies from pati…
Effective Correlates of Motor Imagery Performance based on Default Mode Network in Resting-State
Jae-Geun Yoon, Minji Lee
Motor imagery based brain-computer interfaces (MI-BCIs) allow the control of devices and communication by imagining different muscle movements. However, most studies have reported…
Neural Oscillations for Encoding and Decoding Declarative Memory using EEG Signals
Jenifer Kalafatovich, Minji Lee
Declarative memory has been studied for its relationship with remembering daily life experiences. Previous studies reported changes in power spectra during encoding phase related t…
A BCI based Smart Home System Combined with Event-related Potentials and Speech Imagery Task
Hyeong-Jin Kim, Min-Ho Lee, Minji Lee
Recently, smart home systems based on brain-computer interface (BCI) has attracted a wide range of interests in both industry and academia. However, the current BCI system has seve…
Decoding Visual Responses based on Deep Neural Networks with Ear-EEG Signals
Young-Eun Lee, Minji Lee
Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment. However, the electroencephalography signals are distorted by movemen…