• 2024

1. B.-H. Lee, J.-H. Cho, B.-H. Kwon, M. Lee, and S.-W. Lee, "Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control," IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024. (Accepted)

2. M. Lee†, H. Kang†, S.-H. Yu, H. Cho, J. Oh*, G. v. d. Lande, O. Gosseries, and J.-H. Jeong*, "Automatic Sleep Stage Classification Using Nasal Pressure Decoding Based on a Multi-Kernel Convolutional BiLSTM Network," IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 32, No. 1, 2024, pp. 2533-2544. (2023-JCR-IF: 4.800, Top 2.70% in Rehabilitation) 

3. M. Lee, H.-Y. Park, W. Park, K.-T. Kim, Y.-H. Kim*, and J.-H. Jeong*, "Multi-task Heterogeneous Ensemble Learning-based Cross-Subject EEG Classification under Stroke Patients," IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 32, No. 1, 2024, pp. 1767-1778. (2022-JCR-IF: 4.900, Top 5.10% in Rehabilitation) 

• 2023

4. Y. K. Kim, J. H. Koo, S. J. Lee, H. S. Song*, and M. Lee*, "Explainable Artificial Intelligence Warning Model Using an Ensemble Approach for In-Hospital Cardiac Arrest Prediction: Retrospective Cohort Study," Journal of Medical Internet Research, Vol. 25, No. 1, 2023, pp. e48244. (2022-JCR-IF: 7.4, Top 2.4% in Health Care Sciences & Services) 

5. M. Lee, H.-G. Kwak, H.-J. Kim, D.-O. Won and S.-W. Lee, "SeriesSleepNet: An EEG Time Series Model with Partial Data Augmentation for Automatic Sleep Stage Scoring," Frontiers in Physiology, Vol. 14, No. 1, 2023, pp. 1188678. (2022-JCR-IF: 4.0, Top 24.7% in Physiology) 

• ~2022

6. J. Kalafatovich, M. Lee, and S.-W. Lee, “Learning Spatiotemporal Graph Representations for Visual Perception using EEG Signals,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 31, No. 1, 2022, pp. 97-108. 

7. M. Lee, L. R. Sanz, A. Barra, A. Wolff, J. O. Nieminen, M. Boly, O. Bodart, J. Annen, A. Thibaut, M. Rosanova, S. Casarotto, R. Panda, V. Bonhomme, M. Massimini, G. Tononi, S. Laureys, O. Gosseries, and S.-W. Lee, “Quantifying Arousal and Awareness in Altered States of Consciousness using Interpretable Deep Learning,” Nature Communications, Vol. 13, No. 1, 2022, pp.1-14. (2021-JCR-IF: 17.694, Top 7.43% in Multidisciplinary Sciences) 

8. J. Kalafatovich, M. Lee, and S.-W. Lee, “Decoding Declarative Memory Process for Predicting Memory Retrieval based on Source Localization,” PLoS One, Vol. 17, No. 9, 2022, pp. e0274101.

9. M. Lee, Y.-H. Kim, and S.-W. Lee, “Motor Impairment in Stroke Patients is Associated with Network Properties During Consecutive Motor Imagery,” IEEE Transactions on Biomedical Engineering, Vol. 69, No. 8, 2022, pp.2604-2615. 

10. Y. K. Kim, M. Lee, H. S. Song, and S.-W. Lee, “Automatic Cardiac Arrhythmia Classification Using Residual Network Combined with Long Short-term Memory,” IEEE Transactions on Instrumentation and Measurement, Vol. 71, No. 4005817, 2022, pp. 1-17. 

11. Y.-E. Lee, G.-H. Shin, M. Lee, and S.-W. Lee, “Mobile BCI Dataset of Scalp- and Ear-EEGs with ERP and SSVEP Paradigms While Standing, Walking, and Running,” Scientific Data, Vol. 8, No. 1, 2021, pp. 1-12. 

12. D.-Y. Lee†, M. Lee†, and S.-W. Lee, “Decoding Imagined Speech based on Deep Metric Learning for Intuitive BCI Communication,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 29, 2021, pp. 1363-1374. (2020-JCR-IF: 3.802, Top 9.89% in Rehabilitation) 

13. M. Lee†, J.-H. Jeong†, Y.-H. Kim, and S.-W. Lee, “Decoding Figure Tapping with the Affected Hand in Chronic Stroke Patients During Motor Imagery and Execution,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 29, 2021, pp. 1099-1109. (2020-JCR-IF: 3.802, Top 9.89% in Rehabilitation) 

14. S.-H. Lee, M. Lee, and S.-W. Lee, “Neural Decoding of Imagined Speech and Visual Imagery as Intuitive Paradigms for BCI Communication,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 28, No. 12, 2020, pp. 2647-2659. 

15. M. Lee†, J.-G. Yoon†, and S.-W. Lee, “Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling,” Frontiers in Human Neuroscience, Vol. 14, No. 321, 2020, pp. 1-15. 

16. M. Lee†, G.-H. Shin†, and S.-W. Lee, “Frontal EEG Asymmetry of Emotion for the Same Auditory Stimulus,” IEEE Access, Vol. 8, 2020, pp. 107200-107213. 

17. M. Lee, C.-B. Song, G.-H. Shin, and S.-W. Lee, “Possible Effect of Binaural Beat Combined with Autonomous Sensory Meridian Response for Inducing Sleep,” Frontiers in Human Neuroscience, Vol. 13, No. 425, 2019, pp. 1-16. 

18. M. Lee, B. Baird, O. Gosseries, J. O. Nieminen, M. Boly, B. R. Postle, G. Tononi, and S.-W. Lee, “Connectivity Differences between Consciousness and Unconsciousness in Non-rapid Eye Movement Sleep: a TMS-EEG Study,” Scientific Reports, Vol. 9, No. 1, 2019, pp. 5175. 

19. M. Lee, R. D. Sanders, S.-K. Yeom, D.-O. Won, K.-S. Seo, H. J. Kim, G. Tononi, and S.-W. Lee, “Network Properties in Transitions of Consciousness during Propofol-induced Sedation,” Scientific Reports, Vol. 7, No. 1, 2017, pp. 16791.

20. M. Lee, C.-H. Park, C.-H. Im, J.-H. Kim, G.-H. Kwon, L. Kim, W. H. Chang, and Y.-H. Kim, “Motor Imagery Learning across a Sequence of Trials in Stroke Patients,” Restorative Neurology and Neuroscience, Vol. 34, No. 4, 2016, pp. 635-345. 

21. M. Lee, Y.-H. Kim, C.-H. Im, J.-H. Kim, C.-H. Park, W. H. Chang, and A. Lee, “What is the Optimal Anodal Electrode Position for Inducing Corticomotor Excitability Changes in Transcranial Direct Current Stimulation?,” Neuroscience Letters, Vol. 584, No. 1, 2015, pp. 347-350.

22. J. Lee, M. Lee, D.-S. Kim, and Y.-H. Kim, “Functional Reorganization and Prediction of Motor Recovery after Stroke: Graph Theoretical Analysis of Functional Networks,” Restorative Neurology and Neuroscience, Vol. 33, No. 6, 2015, pp. 785-793. 

23. C.-H. Park, W. H. Chang, M. Lee, G.-H. Kwon, L. Kim, S. T. Kim, and Y.-H. Kim, “Which Motor Cortical Region Best Predicts Imagined Movement?,” NeuroImage, Vol. 113, No. 1, 2015, pp. 101-110.

24. C.-H. Park, W. H. Chang, M. Lee, G.-H. Kwon, L. Kim, S. T. Kim, and Y.-H. Kim, “Predicting the Performance of Motor Imagery in Stroke Patients: Multivariate Pattern Analysis of Functional MRI Data,” Neurorehabilitation and Neural Repair, Vol. 29, No. 3, 2015, pp. 247-254.

† Equal contributor * Corresponding author