Publications

Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks
Please refer to our papers,
O.-Y. Kwon, M.-H. Lee, C. Guan, and S.-W. Lee, “Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks,” IEEE Trans. on Neural Networks and Learning Systems, Vol. 31, No. 10, 2020, pp. 3839-3852.
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A BCI based Smart Home System Combined with Event-related Potentials and Speech Imagery Task
Please refer to our papers,
H.-J. Kim, M.-H. Lee, and M. Lee, “A BCI based Smart Home System Combined with Event-related Potentials and Speech Imagery Task,” Proc. 8th IEEE International Winter Conference on Brain-Computer Interface, Korea, Feb. 26-28, 2020.

Robust Detection of Event-Related Potentials in a User-Voluntary Short-Term Imagery Task
Please refer to our papers,
M.-H. Lee, J. Williamson, Y.-J. Kee, S. Fazli, and S.-W. Lee, “Robust Detection of Event-Related Potentials in a User-Voluntary Short-Term Imagery Task,” PLOS ONE, Vol. 14, No. 12, 2019, e0226236.
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EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms: An Investigation into BCI Illiteracy
Please refer to our papers,
M.-H. Lee, O.-Y. Kwon, Y.-J. Kim, H.-K. Kim, Y.-E. Lee, J. Williamson, S. Fazli, and S.-W. Lee, “EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms: An Investigation into BCI Illiteracy,” GigaScience, Vol. 8, No. 5, 2019, pp. 1-16.
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Changes in Fatigue and EEG Amplitude during a Longtime Use of Brain-Computer Interface
Please refer to our papers,
S.-P. Seo, M.-H. Lee, J. Williamson, S.-W. Lee, “Changes in Fatigue and EEG Amplitude during a Longtime Use of Brain-Computer Interface,” Proc. of the 7th International Winter Conference on Brain-Computer Interface, Korea, Feb. 18-20, 2019.

The Effect of Neurofeedback Training in Virtual and Real Environments based on BCI
Please refer to our papers,
D.-K. Han, M.-H. Lee, J. Williamson, S.-W. Lee, “The Effect of Neurofeedback Training in Virtual and Real Environments based on BCI,” Proc. of the 7th International Winter Conference on Brain-Computer Interface, Korea, Feb. 18-20, 2019.

Mental Fatigue in Central and Peripheral Field SSVEP and Its Effects on ERP Responses
Please refer to our papers,
M.-H. Lee, J. Williamson, Y.-E. Lee, and S.-W. Lee, “Mental Fatigue in Central and Peripheral Field SSVEP and Its Effects on ERP Responses,” NeuroReport, Vol. 29, 2018, pp. 1301-1308.
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A High Performance Spelling System based on EEG-EOG Signals with Visual Feedback
Please refer to our papers,
M.-H. Lee, J. Williamson, D.-O. Won, S. Fazli and S.-W. Lee, “A High Performance Spelling System based on EEG-EOG Signals with Visual Feedback,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 26, No. 7, 2018.
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A Novel BCI Speller Combining Dot-based Visual Stimuli and User Voluntary Sound-Imagery Task
Please refer to our papers,
H.-K. Kim, M.-H. Lee, and S.-W. Lee, “A Novel BCI Speller Combining Dot-based Visual Stimuli and User Voluntary Sound-Imagery Task,” Proc. of the 7th International BCI Meeting, California, USA, May, 2018.

A Hierarchical Classification Strategy for Robust Detection of PassiveActive Mental State Using User-Voluntary Pitch Imagery
Please refer to our papers,
M.-H. Lee and S.-W. Lee, “A Hierarchical Classification Strategy for Robust Detection of PassiveActive Mental State Using User-Voluntary Pitch Imagery,” Proc. of The 4th Asian Conference on Pattern Recognition, Nanjing, China, 26-29, 2017.

Self-Paced Training on Motor Imagery-Based BCI for Minimal Calibration
Please refer to our papers,
M.-H. Lee and S.-W. Lee, “Self-Paced Training on Motor Imagery-Based BCI for Minimal Calibration,” Proc. of 2017 International Conference on Systems, Man and Cybernetics, Nanjing, China, 26-29, 2017.

A Brain-Computer Interface Speller using Peripheral Stimulus-based SSVEP and P300
Please refer to our papers,
M.-H. Lee and S.-W. Lee, “A Brain-Computer Interface Speller using Peripheral Stimulus-based SSVEP and P300,” Proc. of the 5th International Winter Conference on Brain-Computer Interface, Korea, Jan. 9-11, 2017.

Quantifying Movement Intentions with Multimodal Neuroimaging for FES-based Rehabilitation
Please refer to our papers,
M.-H. Lee, B.-J. Kim, and S.-W. Lee, “Quantifying Movement Intentions with Multimodal Neuroimaging for FES-based Rehabilitation,” NeuroReport, Vol. 27, Issue. 2, 2015, pp. 61-67.
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A Multi-Modal Neuroimaging Algorithm for Hybrid BCI: Source Code
Please refer to our papers,
M.-H. Lee, S. Fazli, J. Mehnert, and S.-W. Lee, “Subject-Dependent Classification for Robust Idle State Detection using Multi-Modal Neuroimaging and Data-Fusion Techniques in BCI, Pattern Recognition, Vol. 48, No. 8, 2015, pp. 2725-2737.
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A BCI Speller based on SSVEP using High Frequency Stimuli: DB: Database
Please refer to our papers,
D.-O. Won, H. H. Zhang, S. Dahne, K. Muller, and S.-W. Lee, “Effect of Higher Frequency on Steady State Visual Evoked Potential based Brain-Computer Interface,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, Vol. 13, No. 1, 2015, pp. 016014.
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Long Distance Heterogeneous Face (LDHF) DB: Database
Please refer to our paper,
D. Kang, H. Han, A. K. Jain, and S.-W. Lee, “Nighttime Face Recognition at Large Standoff: Cross-Distance and Cross-Spectral Matching,” Pattern Recognition, Vol. 47, No. 12, 2014, pp. 3750-3766.
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Person Authentication DB using Face-Specific Visual Self-Representation: Database
Please refer to our paper,
S.-K Yeom, H. -I. Suk, and S. -W. Lee , “Person Authentication from neural Activity of Face-Specific Visual Self-Representation,” Pattern Recognition, Vol. 46, No. 4, 2013, pp. 1159-1169.
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