Estimation on the development of cerebral edema from computed tomography preliminary studies for pediatric traumatic brain injury patients. - Hakseung Kim; Young-Tak Kim; Dong-Joo Kim (BCI, 2013)

Morphological landmark detection in arterial blood pressure and intracranial pressure: Preliminary procedures for intracranial pressure waveform analysis. - Eun-Jin Jeong; Hakseung Kim; Xiao ke Yang; Hack-Jin Lee; Dae-Hyeon Park; Young-Tak Kim; Dong-Joo Kim (BCI, 2015)

Automated phase segmentation in cerebrospinal fluid infusion test. - Young-Tak Kim; Hakseung Kim; Dae-Hyeon Park; Xiao ke Yang; Hack-Jin Lee; Eun-Jin Jeong; Dong-Joo Kim (BCI, 2015)

The age-related difference in computed tomography density distribution: A preliminary report. - Hakseung Kim; Xiao ke Yang; Young-Tak Kim; Hack-Jin Lee; Eun-Jin Jeong; Dae-Hyeon Park; Dong-Joo Kim (BCI, 2015)

Noninvasive assessment of intracranial pressure using functional matrix estimation method. - Hack-Jin Lee; Hakseung Kim; Dong-Ho Lee; Xiao ke Yang; Eun-Jin Jeong; Dae-Hyeon Park; Young-Tak Kim; Dong-Joo Kim (BCI, 2015)

Automated artefact elimination in computed tomography: A preliminary report for traumatic brain injury and stroke. - Dae-Hyeon Park; Hakseung Kim; Young-Tak Kim; Xiao ke Yang; Hack-Jin Lee; Eun-Jin Jeong; Dong-Joo Kim (BCI, 2015)

Semi-automatic designation and segmentation of vertebra and spinal cord in spinal MR imaging: A preliminary report. - Xiao ke Yang; Hakseung Kim; Seong Yi; Eun-Jin Jeong; Dae-Hyeon Park; Young-Tak Kim; Hack-Jin Lee; Dong-Joo Kim (BCI, 2015)

Finite element analysis for normal pressure hydrocephalus: The effects of the integration of sulci. - Hakseung Kim; Dae-Hyeon Park; Seong Yi; Eun-Jin Jeong; Byung C. Yoon; Marek Czosnyka; Michael P. F. Sutcliffe; Dong-Joo Kim (Medical Image Anal., 2015)

Morphological Feature Extraction From a Continuous Intracranial Pressure Pulse via a Peak Clustering Algorithm. - Hack-Jin Lee; Eun-Jin Jeong; Hakseung Kim; Marek Czosnyka; Dong-Joo Kim (IEEE Trans. Biomed. Eng., 2016)

Robust arterial blood pressure onset detection method from signal artifacts. - Seung-Bo Lee; Eun-Suk Song; Hakseung Kim; Dong-Joo Kim (BCI, 2018)

Automated artifact elimination of physiological signals using a deep belief network: An application for continuously measured arterial blood pressure waveforms. - Yunsik Son 001; Seung-Bo Lee; Hakseung Kim; Eun-Suk Song; Hyub Huh; Marek Czosnyka; Dong-Joo Kim (Inf. Sci., 2018)

Exploring the Number of Repetitions in Trials for the Performance Convergence of Classification in Motor Imagery Task with Hand-Grasping. - Young-Tak Kim; Seung-Bo Lee; Hakseung Kim; Ji-Hoon Jeong; Seong-Whan Lee; Dong-Joo Kim (BCI, 2019)

Recurrent convolutional neural network model based on temporal and spatial feature for motor imagery classification. - Seung-Bo Lee; Hakseung Kim; Ji-Hoon Jeong; In-Nea Wang; Seong-Whan Lee; Dong-Joo Kim (BCI, 2019)

Reduced Burden of Individual Calibration Process in Brain-Computer Interface by Clustering the Subjects based on Brain Activation. - Young-Tak Kim; Seho Lee; Hakseung Kim; Seung-Bo Lee; Seong-Whan Lee; Dong-Joo Kim (SMC, 2019)

Classification of the Motion Artifacts in Near-infrared Spectroscopy Based on Wavelet Statistical Feature. - Seung-Bo Lee; Hakseung Kim; Seho Lee; Hyun-Ji Kim; Seong-Whan Lee; Dong-Joo Kim (SMC, 2019)

Comparative analysis of features extracted from EEG spatial, spectral and temporal domains for binary and multiclass motor imagery classification. - Seung-Bo Lee; Hyun-Ji Kim; Hakseung Kim; Ji-Hoon Jeong; Seong-Whan Lee; Dong-Joo Kim (Inf. Sci., 2019)

Comparative Analysis of NIRS-EEG Motor Imagery Data Using Features from Spatial, Spectral and Temporal Domain. - Hyun-Ji Kim; In-Nea Wang; Young-Tak Kim; Hakseung Kim; Dong-Joo Kim (BCI, 2020)

Importance of Reliable EEG Data in Motor Imagery Classification: Attention Level-based Approach. - Seho Lee; Young-Tak Kim; Seung-Ouk Hwang; Hakseung Kim; Dong-Joo Kim (BCI, 2020)

A Comparison of Oversampling Methods for Constructing a Prognostic Model in the Patient with Heart Failure. - Young-Tak Kim; Dong-Kyu Kim; Hakseung Kim; Dong-Joo Kim (ICTC, 2020)

An Ensemble Deep Learning Approach for Sleep Stage Classification via Single-channel EEG and EOG. - In-Nea Wang; Choel-Hui Lee; Hyun-Ji Kim; Hakseung Kim; Dong-Joo Kim (ICTC, 2020)

Complex Motor Imagery-based Brain-Computer Interface System: A Comparison Between Different Classifiers. - Seung-Bo Lee; Min-Kyung Jung; Hakseung Kim; Seong-Whan Lee; Dong-Joo Kim (SMC, 2020)

Lateralization of alpha oscillation under preparation Lead to Efficiency of Motor Imagery: Related with Performance of Classification. - Seho Lee; Choel-Hui Lee; Hakseung Kim; Dong-Joo Kim (SMC, 2020)

Phase Transition in previous Motor Imagery affects Efficiency of Motor Imagery based Brain-computer Interface. - Min-Kyung Jung; Seho Lee; In-Nea Wang; Ha Yoon Song; Hakseung Kim; Dong-Joo Kim (BCI, 2021)

Sequential Transfer Learning via Segment After Cue Enhances the Motor Imagery-based Brain-Computer Interface. - Dong-Kyu Kim; Young-Tak Kim; Hee-Ra Jung; Hakseung Kim; Dong-Joo Kim (BCI, 2021)

Improving Sleep Stage Classification Performance by Single-Channel EEG Data Augmentation via Spectral Band Blending. - Choel-Hui Lee; Hyun-Ji Kim; Jae-Wook Heo; Hakseung Kim; Dong-Joo Kim (BCI, 2021)

Prediction of Life-Threatening Intracranial Hypertension During the Acute Phase of Traumatic Brain Injury Using Machine Learning. - Hack-Jin Lee; Hakseung Kim; Young-Tak Kim; Kanghee Won; Marek Czosnyka; Dong-Joo Kim (IEEE J. Biomed. Health Informatics, 2021)

Importance of the Quantitative Change of EEG Theta/Beta Ratio Between Preparation and Motor Imagery: Correlation with the Performance of Classification. - Joung-Woo Hyung; Seho Lee; Hakseung Kim; Dong-Joo Kim (BCI, 2022)

DeepCNAP: A Deep Learning Approach for Continuous Noninvasive Arterial Blood Pressure Monitoring Using Photoplethysmography. - Dong-Kyu Kim; Young-Tak Kim; Hakseung Kim; Dong-Joo Kim (IEEE J. Biomed. Health Informatics, 2022)

EEG-Based Multioutput Classification of Sleep Stage and Apnea Using Deep Learning. - Donghyeok Jo; Choel-Hui Lee; Hakseung Kim; Hayom Kim; Jung-Bin Kim; Dong-Joo Kim (BCI, 2023)

Interpretability of Hybrid Feature Using Graph Neural Networks from Mental Arithmetic Based EEG. - Min-Kyung Jung; Hakseung Kim; Seho Lee; Jung-Bin Kim; Dong-Joo Kim (BCI, 2023)

SleepExpertNet: high-performance and class-balanced deep learning approach inspired from the expert neurologists for sleep stage classification. - Choel-Hui Lee; Hyun-Ji Kim; Young-Tak Kim; Hakseung Kim; Jung-Bin Kim; Dong-Joo Kim (J. Ambient Intell. Humaniz. Comput., 2023)

Correction to: SleepExpertNet: high‑performance and class‑balanced deep learning approach inspired from the expert neurologists for sleep stage classification. - Choel-Hui Lee; Hyun-Ji Kim; Young-Tak Kim; Hakseung Kim; Jung-Bin Kim; Dong-Joo Kim (J. Ambient Intell. Humaniz. Comput., 2023)

Prediction of Serious Intracranial Hypertension from Low-Resolution Neuromonitoring in Traumatic Brain Injury: An Explainable Machine Learning Approach. - Min-Kyung Jung; Daesun Ahn; Chan Min Park; Eun Jin Ha; Tae-Hoon Roh; Nam Kyu You; Dukyong Yoon; Hakseung Kim; Se-Hyuk Kim; Dong-Joo Kim (IEEE J. Biomed. Health Informatics, 2023)

Effects of altered functional connectivity on motor imagery brain-computer interfaces based on the laterality of paralysis in hemiplegia patients. - Seho Lee; Hakseung Kim; Jung-Bin Kim; Dong-Joo Kim (Comput. Biol. Medicine, 2023)

Enhancing Everyday Seizure Detection: A Channel Reduction Approach. - Minju Kim; Donghyeok Jo; In-Nea Wang; Hakseung Kim; Jung-Bin Kim; Dong-Joo Kim (BCI, 2024)

Negative-Sample-Free Contrastive Self-Supervised Learning for Electroencephalogram-Based Motor Imagery Classification. - In-Nea Wang; Choel-Hui Lee; Hakseung Kim; Dong-Joo Kim (IEEE Access, 2024)

NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG. - Cheol-Hui Lee; Hakseung Kim; Hyun-jee Han; Min-Kyung Jung; Byung C. Yoon; Dong-Joo Kim (CoRR, 2024)

Hyperosmolar therapy response in traumatic brain injury: Explainable artificial intelligence based long-term time series forecasting approach. - Min-Kyung Jung; Tae-Hoon Roh; Hakseung Kim; Eun Jin Ha; Dukyong Yoon; Chan Min Park; Se-Hyuk Kim; Nam Kyu You; Dong-Joo Kim (Expert Syst. Appl., 2024)

Detection of Alzheimer's Disease and Frontotemporal Dementia: An Explainable Machine Learning Approach Using EEG Signals. - Hwa-Yeon Lee; Min-Kyung Jung; Choel-Hui Lee; Hakseung Kim; Dong-Joo Kim (BCI, 2025)

NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG application. - Choel-Hui Lee; Daesun Ahn; Hakseung Kim; Eun Jin Ha; Jung-Bin Kim; Dong-Joo Kim (Expert Syst. Appl., 2025)

Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework. - Cheol-Hui Lee; Hakseung Kim; Byung C. Yoon; Dong-Joo Kim (CoRR, 2025)

Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection. - Tae-Seong Han; Jae-Wook Heo; Hakseung Kim; Cheol-Hui Lee; Hyub Huh; Eue-Keun Choi; Dong-Joo Kim (CoRR, 2025)