• 2025

1. S.-J. Kweon, U. Berdica, H. Park, M. A. Akram, M. Lee, and S. Ha*, "Asynchronous Quadrature-phase Undersampling Technique for Wide-frequency Impedance Measurement," IEEE Transactions on Instrumentation and Measurement, Early Access, 2025. (2024-JCR-IF: 5.9, Top 8.2% in Instruments & Instrumentation)

2. Y. Jang, J. Jeong, Y. K. Kim , D.-H. Kim, W. Park, L. Kim, Y.-H. Kim, and M. Lee*, "DualDyConvNet: Dual-Stream Dynamic Convolution Network via Parameter-Efficient Fine-Tuning for Predicting Motor Prognosis in Subacute Stroke," IEEE Transactions on Neural Systems and Rehabilitation Engineering, Early Access, 2025. (2024-JCR-IF: 5.2, Top 2.00% in Rehabilitation) 

3. Y. K. Kim, M. Lee, S. Song, and S.-W. Lee*, "Local-Global Temporal Fusion Network with an Attention Mechanism for Multiple and Multiclass Arrhythmia Classification," IEEE Transactions on Systems, Man and Cybernetics: Systems, Vol. 55, No. 10, 2025, pp. 6569-6584. (2023-JCR-IF: 8.6, Top 5.40% in Automation & Control Systems) 

4. B.-H. Kwon, M. Lee, and S.-W. Lee*, "Functional Connectivity Guided Deep Neural Network for Decoding High-Level Visual Imagery," Expert Systems with Applications, Vol. 294, No. 1, 2025, pp 128734.  (2023-JCR-IF: 7.5, Top 5.2% in Operations Research & Management Science)

5. G.-H. Shin, Y.-S. Kweon, M. Lee, K.-Y. Jung, and S.-W. Lee*, "Quantifying Sleep Quality Through Delta-Beta Coupling Across Sleep and Wakefulness," IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 33, No. 1, 2025, pp. 1907-1917. (2023-JCR-IF: 4.800, Top 2.70% in Rehabilitation) 

6. M. Lee and S. Laureys, "Charting the Frontiers of Brain Connectivity: From Rehabilitation to Imagination," Brain Connectivity, Vol. 15, No. 2, 2025, pp. 57-58. 

7. Y. K. Kim, I. Choi, S. J. Lee, H.-B. Shin, G. C. Kim, H. S. Song, M. Lee*, and S.-W. Lee*, "A Brain-Inspired Model for Multi-Step Forecasting of Malignant Arrhythmias," Expert Systems with Applications, Vol. 270, No. 126373, 2025, pp. 1-15. (2023-JCR-IF: 7.5, Top 5.2% in Operations Research & Management Science)

8. P. Cardone, A. Bonhomme, V. Bonhomme, N. Lejeune, C. Staquet, A. Defresne, N. Alnagger, P. Ezan, M. Lee, A. Piarulli, S. V. Goethem, J. Montupil, A. Thibaut, C. Martial*, and O. Gosseries*, "A Pilot Human Study Using Ketamine To Treat Disorders of Consciousness," iScience, Vol. 28, No. 1, 2025, pp. 111639. (2023-JCR-IF: 4.6, Top 13.8% in Multidisciplinary Sciences)

9. D.-K. Han, M. Lee, and S.-W. Lee*, "Multi-Layer Prototype Learning with Dirichlet Mixup for Open-Set EEG Recognition," Expert Systems with Applications, Vol. 266, No. 126047, 2025, pp. 1-12. (2023-JCR-IF: 7.5, Top 5.2% in Operations Research & Management Science)

• 2024

10. M. Lee, W. Park, E. Park, S-J. Kweon*, and Y.-H. Kim*, "Neuromodulation Effect According to Lesion Location After Dual-Mode Brain Stimulation in Patients with Subacute Stroke: A Preliminary Study," Applied Sciences, Vol. 14, No. 21, 2024, pp. 1-15. (2023-JCR-IF: 2.5, Top 24.3% in Engineering, Multidisciplinary)

11. M. Lee and S. Laureys, "Artificial Intelligence and Machine Learning in Disorders of Consciousness," Current Opinion in Neurology, Vol. 37, No. 6, 2024, pp. 614-620.  (2023-JCR-IF: 4.1, Top 16.8% in Clinical Neurology)

12. Y. K. Kim, W.-D. Seo, S. J. Lee, J. H. Koo, K. C. Kim, H. S. Song, and M. Lee*, "Early Prediction of Cardiac Arrest in the Intensive Care Unit using Explainable Machine Learning: Retrospective Study, " Journal of Medical Internet Research, Vol. 26, No. 1, 2024, pp. e62890. (2023-JCR-IF: 6.7, Top 4.9% in Health Care Sciences & Services) 

13. 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, Vol. 32, No. 1, 2024, pp. 2793-2804. (2023-JCR-IF: 4.800, Top 2.70% in Rehabilitation) 

14. 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) 

15. 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

16. 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) 

17. 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

18. 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. 

19. 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) 

20. 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.

21. 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. 

22. 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. 

23. 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. 

24. 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) 

25. 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) 

26. 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. 

27. 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. 

28. 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. 

29. 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. 

30. 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. 

31. 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.

32. 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. 

33. 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.

34. 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. 

35. 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.

36. 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