| Performance variation in motor imagery brain–computer interface: a brief review M Ahn, SC Jun Journal of neuroscience methods 243, 103-110, 2015 | 366 | 2015 |
| EEG datasets for motor imagery brain–computer interface H Cho, M Ahn, S Ahn, M Kwon, SC Jun GigaScience 6 (7), gix034, 2017 | 322 | 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 | 263 | 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 | 241 | 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 | 111 | 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 | 89 | 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 | 75 | 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 | 58 | 2021 |
| 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 | 57 | 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 | 51 | 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 | 46 | 2012 |
| EEG dataset for RSVP and P300 speller brain-computer interfaces K Won, M Kwon, M Ahn, SC Jun Scientific data 9 (1), 388, 2022 | 43 | 2022 |
| 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 | 40 | 2020 |
| 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 | 40 | 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 | 39 | 2018 |
| 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 | 38 | 2019 |
| 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 | 28 | 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 | 27 | 2021 |
| A step-by-step tutorial for a motor imagery–based BCI H Cho, M Ahn, M Kwon, SC Jun Brain–Computer Interfaces Handbook, 445-460, 2018 | 21 | 2018 |
| 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 | 19 | 2013 |
Sung Chan JunAI Graduate School/School of EECS, Gwangju Institute of Science and TechnologyVerified email at gist.ac.kr
