Minkyu Ahn
Minkyu Ahn
Associate Professor, Handong Global University
Verified email at - Homepage
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
EEG datasets for motor imagery brain–computer interface
H Cho, M Ahn, S Ahn, M Kwon, SC Jun
GigaScience 6 (7), gix034, 2017
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), e80886, 2013
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
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
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
Diagnostic classification and biomarker identification of Alzheimer’s disease with random forest algorithm
M Song, H Jung, S Lee, D Kim, M Ahn
Brain sciences 11 (4), 453, 2021
Increasing session-to-session transfer in a brain–computer interface with on-site background noise acquisition
H Cho, M Ahn, K Kim, SC Jun
Journal of neural engineering 12 (6), 066009, 2015
P300 brain–computer interface-based drone control in virtual and augmented reality
S Kim, S Lee, H Kang, S Kim, M Ahn
Sensors 21 (17), 5765, 2021
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
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
User’s self-prediction of performance in motor imagery brain–computer interface
M Ahn, H Cho, S Ahn, SC Jun
Frontiers in human neuroscience 12, 59, 2018
CNN with large data achieves true zero-training in online P300 brain-computer interface
J Lee, K Won, M Kwon, SC Jun, M Ahn
IEEE Access 8, 74385-74400, 2020
P300 speller performance predictor based on RSVP multi-feature
K Won, M Kwon, S Jang, M Ahn, SC Jun
Frontiers in human neuroscience 13, 261, 2019
EEG dataset for RSVP and P300 speller brain-computer interfaces
K Won, M Kwon, M Ahn, SC Jun
Scientific data 9 (1), 388, 2022
Use of both eyes-open and eyes-closed resting states may yield a more robust predictor of motor imagery BCI performance
M Kwon, H Cho, K Won, M Ahn, SC Jun
Electronics 9 (4), 690, 2020
Alpha and high gamma phase amplitude coupling during motor imagery and weighted cross-frequency coupling to extract discriminative cross-frequency patterns
D Gwon, M Ahn
NeuroImage 240, 118403, 2021
Localization of coherent sources by simultaneous MEG and EEG beamformer
JH Hong, M Ahn, K Kim, SC Jun
Medical & biological engineering & computing 51, 1121-1135, 2013
Calibration time reduction through source imaging in brain computer interface (BCI)
M Ahn, H Cho, SC Jun
HCI International 2011–Posters’ Extended Abstracts: International Conference …, 2011
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