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Hakseung Kim
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- affiliation: Korea University, Seoul, South Korea
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2020 – today
- 2025
- [j13]Choel-Hui Lee
, Daesun Ahn
NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG application. Expert Syst. Appl. 261: 125364 (2025) - 2024
- [j12]In-Nea Wang
Negative-Sample-Free Contrastive Self-Supervised Learning for Electroencephalogram-Based Motor Imagery Classification. IEEE Access 12: 132714-132728 (2024) - [j11]Minkyung Jung, Tae-Hoon Roh, Hakseung Kim, Eun Jin Ha, Dukyong Yoon, Chan Min Park, Se-Hyuk Kim, Nam Kyu You, Dong-Joo Kim:
Hyperosmolar therapy response in traumatic brain injury: Explainable artificial intelligence based long-term time series forecasting approach. Expert Syst. Appl. 255: 124795 (2024) - [c25]Minju Kim, Donghyeok Jo, In-Nea Wang, Hakseung Kim, Jung-Bin Kim
Enhancing Everyday Seizure Detection: A Channel Reduction Approach. BCI 2024: 1-4 - [i1]Cheol-Hui Lee, Hakseung Kim, Hyun-jee Han, Minkyung Jung, Byung C. Yoon, Dong-Joo Kim:
NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG. CoRR abs/2404.17585 (2024) - 2023
- [j10]Seho Lee
Effects of altered functional connectivity on motor imagery brain-computer interfaces based on the laterality of paralysis in hemiplegia patients. Comput. Biol. Medicine 166: 107435 (2023) - [j9]Choel-Hui Lee, Hyun-Ji Kim, Young-Tak Kim, Hakseung Kim, Jung-Bin Kim, Dong-Joo Kim:
SleepExpertNet: high-performance and class-balanced deep learning approach inspired from the expert neurologists for sleep stage classification. J. Ambient Intell. Humaniz. Comput. 14(6): 8067-8083 (2023) - [j8]Choel-Hui Lee, Hyun-Ji Kim, Young-Tak Kim, Hakseung Kim, Jung-Bin Kim
Correction to: SleepExpertNet: high‑performance and class‑balanced deep learning approach inspired from the expert neurologists for sleep stage classification. J. Ambient Intell. Humaniz. Comput. 14(6): 8085 (2023) - [j7]Minkyung Jung
Prediction of Serious Intracranial Hypertension from Low-Resolution Neuromonitoring in Traumatic Brain Injury: An Explainable Machine Learning Approach. IEEE J. Biomed. Health Informatics 27(4): 1903-1913 (2023) - [c24]Donghyeok Jo, Choel-Hui Lee, Hakseung Kim, Hayom Kim
EEG-Based Multioutput Classification of Sleep Stage and Apnea Using Deep Learning. BCI 2023: 1-4 - [c23]Minkyung Jung, Hakseung Kim, Seho Lee, Jung-Bin Kim
Interpretability of Hybrid Feature Using Graph Neural Networks from Mental Arithmetic Based EEG. BCI 2023: 1-5 - 2022
- [j6]Dong-Kyu Kim
DeepCNAP: A Deep Learning Approach for Continuous Noninvasive Arterial Blood Pressure Monitoring Using Photoplethysmography. IEEE J. Biomed. Health Informatics 26(8): 3697-3707 (2022) - [c22]Joung-Woo Hyung, Seho Lee
Importance of the Quantitative Change of EEG Theta/Beta Ratio Between Preparation and Motor Imagery: Correlation with the Performance of Classification. BCI 2022: 1-4 - 2021
- [j5]Hack-Jin Lee
Prediction of Life-Threatening Intracranial Hypertension During the Acute Phase of Traumatic Brain Injury Using Machine Learning. IEEE J. Biomed. Health Informatics 25(10): 3967-3976 (2021) - [c21]Minkyung Jung, Seho Lee
Phase Transition in previous Motor Imagery affects Efficiency of Motor Imagery based Brain-computer Interface. BCI 2021: 1-4 - [c20]Dong-Kyu Kim, Young-Tak Kim, Hee-Ra Jung
Sequential Transfer Learning via Segment After Cue Enhances the Motor Imagery-based Brain-Computer Interface. BCI 2021: 1-5 - [c19]Choel-Hui Lee, Hyun-Ji Kim, Jae-Wook Heo, Hakseung Kim, Dong-Joo Kim:
Improving Sleep Stage Classification Performance by Single-Channel EEG Data Augmentation via Spectral Band Blending. BCI 2021: 1-5 - 2020
- [c18]Hyun-Ji Kim, In-Nea Wang, Young-Tak Kim, Hakseung Kim, Dong-Joo Kim:
Comparative Analysis of NIRS-EEG Motor Imagery Data Using Features from Spatial, Spectral and Temporal Domain. BCI 2020: 1-4 - [c17]Seho Lee
Importance of Reliable EEG Data in Motor Imagery Classification: Attention Level-based Approach. BCI 2020: 1-4 - [c16]Young-Tak Kim, Dong-Kyu Kim, Hakseung Kim, Dong-Joo Kim:
A Comparison of Oversampling Methods for Constructing a Prognostic Model in the Patient with Heart Failure. ICTC 2020: 379-383 - [c15]In-Nea Wang, Choel-Hui Lee, Hyun-Ji Kim, Hakseung Kim, Dong-Joo Kim:
An Ensemble Deep Learning Approach for Sleep Stage Classification via Single-channel EEG and EOG. ICTC 2020: 394-398 - [c14]Seung-Bo Lee, Minkyung Jung, Hakseung Kim, Seong-Whan Lee, Dong-Joo Kim:
Complex Motor Imagery-based Brain-Computer Interface System: A Comparison Between Different Classifiers. SMC 2020: 2496-2501 - [c13]Seho Lee
Lateralization of alpha oscillation under preparation Lead to Efficiency of Motor Imagery: Related with Performance of Classification. SMC 2020: 2502-2505
2010 – 2019
- 2019
- [j4]Seung-Bo Lee, Hyun-Ji Kim, Hakseung Kim, Ji-Hoon Jeong
Comparative analysis of features extracted from EEG spatial, spectral and temporal domains for binary and multiclass motor imagery classification. Inf. Sci. 502: 190-200 (2019) - [c12]Young-Tak Kim, Seung-Bo Lee, Hakseung Kim, Ji-Hoon Jeong
Exploring the Number of Repetitions in Trials for the Performance Convergence of Classification in Motor Imagery Task with Hand-Grasping. BCI 2019: 1-4 - [c11]Seung-Bo Lee, Hakseung Kim, Ji-Hoon Jeong
Recurrent convolutional neural network model based on temporal and spatial feature for motor imagery classification. BCI 2019: 1-4 - [c10]Young-Tak Kim, Seho Lee
Reduced Burden of Individual Calibration Process in Brain-Computer Interface by Clustering the Subjects based on Brain Activation. SMC 2019: 2139-2143 - [c9]Seung-Bo Lee, Hakseung Kim, Seho Lee
Classification of the Motion Artifacts in Near-infrared Spectroscopy Based on Wavelet Statistical Feature. SMC 2019: 2144-2148 - 2018
- [j3]Yunsik Son, Seung-Bo Lee, Hakseung Kim, Eun-Suk Song, Hyub Huh
Automated artifact elimination of physiological signals using a deep belief network: An application for continuously measured arterial blood pressure waveforms. Inf. Sci. 456: 145-158 (2018) - [c8]Seung-Bo Lee, Eun-Suk Song, Hakseung Kim, Dong-Joo Kim:
Robust arterial blood pressure onset detection method from signal artifacts. BCI 2018: 1-3 - 2016
- [j2]Hack-Jin Lee, Eun-Jin Jeong, Hakseung Kim, Marek Czosnyka, Dong-Joo Kim
Morphological Feature Extraction From a Continuous Intracranial Pressure Pulse via a Peak Clustering Algorithm. IEEE Trans. Biomed. Eng. 63(10): 2169-2176 (2016) - 2015
- [j1]Hakseung Kim, Dae-Hyeon Park, Seong Yi, Eun-Jin Jeong, Byung C. Yoon
Finite element analysis for normal pressure hydrocephalus: The effects of the integration of sulci. Medical Image Anal. 24(1): 235-244 (2015) - [c7]Eun-Jin Jeong, Hakseung Kim, Xiao ke Yang, Hack-Jin Lee, Dae-Hyeon Park, Young-Tak Kim, Dong-Joo Kim:
Morphological landmark detection in arterial blood pressure and intracranial pressure: Preliminary procedures for intracranial pressure waveform analysis. BCI 2015: 1-3 - [c6]Young-Tak Kim, Hakseung Kim, Dae-Hyeon Park, Xiao ke Yang, Hack-Jin Lee, Eun-Jin Jeong, Dong-Joo Kim:
Automated phase segmentation in cerebrospinal fluid infusion test. BCI 2015: 1-3 - [c5]Hakseung Kim, Xiao ke Yang, Young-Tak Kim, Hack-Jin Lee, Eun-Jin Jeong, Dae-Hyeon Park, Dong-Joo Kim:
The age-related difference in computed tomography density distribution: A preliminary report. BCI 2015: 1-2 - [c4]Hack-Jin Lee, Hakseung Kim, Dong-Ho Lee, Xiao ke Yang, Eun-Jin Jeong, Dae-Hyeon Park, Young-Tak Kim, Dong-Joo Kim:
Noninvasive assessment of intracranial pressure using functional matrix estimation method. BCI 2015: 1-3 - [c3]Dae-Hyeon Park, Hakseung Kim, Young-Tak Kim, Xiao ke Yang, Hack-Jin Lee, Eun-Jin Jeong, Dong-Joo Kim:
Automated artefact elimination in computed tomography: A preliminary report for traumatic brain injury and stroke. BCI 2015: 1-3 - [c2]Xiao ke Yang, Hakseung Kim, Seong Yi, Eun-Jin Jeong, Dae-Hyeon Park, Young-Tak Kim, Hack-Jin Lee, Dong-Joo Kim:
Semi-automatic designation and segmentation of vertebra and spinal cord in spinal MR imaging: A preliminary report. BCI 2015: 1-2 - 2013
- [c1]Hakseung Kim, Young-Tak Kim, Dong-Joo Kim:
Estimation on the development of cerebral edema from computed tomography preliminary studies for pediatric traumatic brain injury patients. BCI 2013: 69-71
Coauthor Index
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