Hyun-Chul Kim

12 papers Misc 1Journal 7Unranked 4
YearRankTypeTitle / Venue / Authors
2025 J jnl
Prediction of White Matter Hyperintensity Progression: A Systematic Comparison of Preprocessing Methods and Deep Learning Models.
IEEE Access
Jin-Su Kim, Yong-Hwan Kim, Sun U. Kwon, Bum-Joon Kim, Hyun-Chul Kim
2025 J jnl
Whole-Brain Task fMRI Decoding Using Stage-Wise Residual-Optimized 3D ConvNeXt With Layer-Global Response Normalization.
IEEE J. Biomed. Health Informatics
Ji-Hye Lim, Hyun-Chul Kim
2020 J jnl
A naturalistic viewing paradigm using 360° panoramic video clips and real-time field-of-view changes with eye-gaze tracking.
NeuroImage
Hyun-Chul Kim, Sangsoo Jin, Sungman Jo, Jong-Hwan Lee
2020 J jnl
fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations.
NeuroImage
Hanh Vu, Hyun-Chul Kim, Minyoung Jung, Jong-Hwan Lee
2019 J jnl
Deep neural network predicts emotional responses of the human brain from functional magnetic resonance imaging.
NeuroImage
Hyun-Chul Kim, Peter A. Bandettini, Jong-Hwan Lee
2019 J jnl
Mediation analysis of triple networks revealed functional feature of mindfulness from real-time fMRI neurofeedback.
NeuroImage
Hyun-Chul Kim, Marion Tegethoff, Gunther Meinlschmidt, Esther Stalujanis, Angelo Belardi, Sungman Jo, Juhyeon Lee, Dong-Youl Kim, Seung-Schik Yoo, Jong-Hwan Lee
2018 — conf
3D convolutional neural network for feature extraction and classification of fMRI volumes.
PRNI
Hanh Vu, Hyun-Chul Kim, Jong-Hwan Lee
2017 Misc conf
Evaluation of weight sparsity regularizion schemes of deep neural networks applied to functional neuroimaging data.
ICASSP
Hyun-Chul Kim, Jong-Hwan Lee
2016 — conf
Evaluation of weight sparsity control during autoencoder training of resting-state fMRI using non-zero ratio and hoyer's sparseness.
PRNI
Hyun-Chul Kim, Jong-Hwan Lee
2015 — conf
Desynchronization of the mu oscillatory activity during motor imagery: A preliminary EEG-fMRI study.
BCI
Hyun-Chul Kim, Jong-Hwan Lee
2015 J jnl
Recursive approach of EEG-segment-based principal component analysis substantially reduces cryogenic pump artifacts in simultaneous EEG-fMRI data.
NeuroImage
Hyun-Chul Kim, Seung-Schik Yoo, Jong-Hwan Lee
2013 — conf
Random Segmentation Based Principal Component Analysis to Remove Residual MR Gradient Artifact in the Simultaneous EEG/fMRI: A Preliminary Study.
ICONIP (3)
Hyun-Chul Kim, Jong-Hwan Lee