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)

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)

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)

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)

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)