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ORCIDHyun-Chul Kim 0002
Person information
- affiliation: Korea University, Department of Brain and Cognitive Engineering, Seoul, Korea
Other persons with the same name
- Hyun-Chul Kim (aka: Hyun-chul Kim, Hyun Chul Kim) — disambiguation page
- Hyun-Chul Kim 0001
![[0000-0001-5437-8285]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0001-5437-8285")
(aka: Hyun-chul Kim 0001) — Sangmyung University, Department of Software, Cheonan, Korea (and 1 more)
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2020 – today
- 2020
[j5]Hyun-Chul Kim, Sangsoo Jin, Sungman Jo, Jong-Hwan Lee:
A naturalistic viewing paradigm using 360° panoramic video clips and real-time field-of-view changes with eye-gaze tracking. NeuroImage 216: 116617 (2020)- [j4]Hanh Vu, Hyun-Chul Kim, Minyoung Jung, Jong-Hwan Lee:
fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations. NeuroImage 223: 117328 (2020)
2010 – 2019
- 2019
- [j3]Hyun-Chul Kim, Peter A. Bandettini, Jong-Hwan Lee:
Deep neural network predicts emotional responses of the human brain from functional magnetic resonance imaging. NeuroImage 186: 607-627 (2019) - [j2]Hyun-Chul Kim, Marion Tegethoff, Gunther Meinlschmidt, Esther Stalujanis, Angelo Belardi, Sungman Jo, Juhyeon Lee, Dong-Youl Kim, Seung-Schik Yoo, Jong-Hwan Lee:
Mediation analysis of triple networks revealed functional feature of mindfulness from real-time fMRI neurofeedback. NeuroImage 195: 409-432 (2019) - 2018
- [c5]Hanh Vu, Hyun-Chul Kim, Jong-Hwan Lee:
3D convolutional neural network for feature extraction and classification of fMRI volumes. PRNI 2018: 1-4 - 2017
- [c4]Hyun-Chul Kim, Jong-Hwan Lee:
Evaluation of weight sparsity regularizion schemes of deep neural networks applied to functional neuroimaging data. ICASSP 2017: 6150-6154 - 2016
- [c3]Hyun-Chul Kim, Jong-Hwan Lee:
Evaluation of weight sparsity control during autoencoder training of resting-state fMRI using non-zero ratio and hoyer's sparseness. PRNI 2016: 1-4 - 2015
- [j1]Hyun-Chul Kim, Seung-Schik Yoo, Jong-Hwan Lee:
Recursive approach of EEG-segment-based principal component analysis substantially reduces cryogenic pump artifacts in simultaneous EEG-fMRI data. NeuroImage 104: 437-451 (2015) - [c2]Hyun-Chul Kim, Jong-Hwan Lee:
Desynchronization of the mu oscillatory activity during motor imagery: A preliminary EEG-fMRI study. BCI 2015: 1-2 - 2013
- [c1]Hyun-Chul Kim, Jong-Hwan Lee:
Random Segmentation Based Principal Component Analysis to Remove Residual MR Gradient Artifact in the Simultaneous EEG/fMRI: A Preliminary Study. ICONIP (3) 2013: 101-107
Coauthor Index
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