Prof. Sung Chan Jun
Cited by
Cited by
Performance variation in motor imagery brain–computer interface: a brief review
M Ahn, SC Jun
Journal of neuroscience methods 243, 103-110, 2015
A review of brain-computer interface games and an opinion survey from researchers, developers and users
M Ahn, M Lee, J Choi, SC Jun
Sensors 14 (8), 14601-14633, 2014
High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery
M Ahn, H Cho, S Ahn, SC Jun
PloS one 8 (11), 2013
An SLA-based cloud computing that facilitates resource allocation in the distributed data centers of a cloud provider
S Son, G Jung, SC Jun
The Journal of Supercomputing 64 (2), 606-637, 2013
Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data
SC Jun, JS George, J Paré-Blagoev, SM Plis, DM Ranken, DM Schmidt, ...
NeuroImage 28 (1), 84-98, 2005
Exploring neuro-physiological correlates of drivers' mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and fNIRS data
S Ahn, T Nguyen, H Jang, JG Kim, SC Jun
Frontiers in human neuroscience 10, 219, 2016
Frequency-difference EIT (fdEIT) using weighted difference and equivalent homogeneous admittivity: validation by simulation and tank experiment
SC Jun, J Kuen, J Lee, EJ Woo, D Holder, JK Seo
Physiological measurement 30 (10), 1087, 2009
Utilization of a combined EEG/NIRS system to predict driver drowsiness
T Nguyen, S Ahn, H Jang, SC Jun, JG Kim
Scientific reports 7, 43933, 2017
Achieving a hybrid brain–computer interface with tactile selective attention and motor imagery
S Ahn, M Ahn, H Cho, SC Jun
Journal of neural engineering 11 (6), 066004, 2014
EEG datasets for motor imagery brain–computer interface
H Cho, M Ahn, S Ahn, M Kwon, SC Jun
GigaScience 6 (7), gix034, 2017
Gamma band activity associated with BCI performance: simultaneous MEG/EEG study
M Ahn, S Ahn, JH Hong, H Cho, K Kim, BS Kim, JW Chang, SC Jun
Frontiers in human neuroscience 7, 848, 2013
Noise robustness analysis of sparse representation based classification method for non-stationary EEG signal classification
Y Shin, S Lee, M Ahn, H Cho, SC Jun, HN Lee
Biomedical Signal Processing and Control 21, 8-18, 2015
Feasibility of approaches combining sensor and source features in brain–computer interface
M Ahn, JH Hong, SC Jun
Journal of neuroscience methods 204 (1), 168-178, 2012
Steady-state somatosensory evoked potential for brain-computer interface—present and future
S Ahn, K Kim, SC Jun
Frontiers in human neuroscience 9, 716, 2016
Comparison of frequency difference reconstruction algorithms for the detection of acute stroke using EIT in a realistic head-shaped tank
B Packham, H Koo, A Romsauerova, S Ahn, A McEwan, SC Jun, ...
Physiological measurement 33 (5), 767, 2012
Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC
SC Jun, JS George, W Kim, J Paré-Blagoev, S Plis, DM Ranken, ...
NeuroImage 40 (4), 1581-1594, 2008
Validation of weighted frequency-difference EIT using a three-dimensional hemisphere model and phantom
S Ahn, TI Oh, SC Jun, JK Seo, EJ Woo
Physiological measurement 32 (10), 1663, 2011
Simple adaptive sparse representation based classification schemes for EEG based brain–computer interface applications
Y Shin, S Lee, M Ahn, H Cho, SC Jun, HN Lee
Computers in biology and medicine 66, 29-38, 2015
Negotiation-based flexible SLA establishment with SLA-driven resource allocation in cloud computing
S Son, SC Jun
2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid …, 2013
Probabilistic forward model for electroencephalography source analysis
SM Plis, JS George, SC Jun, DM Ranken, PL Volegov, DM Schmidt
Physics in Medicine & Biology 52 (17), 5309, 2007
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