Young-Eun Lee

Introduction

Young-Eun Lee

• Post Doctoral Researcher at Stanford University

Career

• Post-Doc at Dept. Psychiatry and Behavioral Sciences, Stanford University, USA, Nov. 2024 - Present
• Post-Doc at Dept. Artificial Intelligence, Korea University, Korea, Mar.2024 - Oct.2024
• Visiting Researcher at Max-Planck Institute for Intelligent Systems, Germany, Aug.2019 - Jan.2020
• Intern at Watson team at IBM Korea, Korea, Jul.2016 - Sep.2016

Education

• Ph.D. at Dept. Brain and Cognitive Engineering, Korea University, Korea, Sep.2017 - Feb.2024
  • Supervisor: Seong-Whan Lee (Distinguished Professor at Korea University)
• B.S. at Electronic & Mechanical Engineering, Handong Global University, Korea, Mar.2012 - Aug.2016

Email: youngeun at stanford dot edu

Google Scholar

GitHub

Linked In

Selected Paper

• Y.-E. Lee, S.-H. Lee, S.-H Kim, and S.-W. Lee, “Towards Voice Reconstruction from EEG during Imagined Speech,” AAAI Conference on Artificial Intelligence (AAAI), 2023.
• Y.-E. Lee, N.-S. Kwak, S.-W. Lee, “A Real-time movement artifact removal method for ambulatory brain-computer interfaces,” IEEE Trans. on Neural Systems & Rehabilitation Engineering, Vol. 28, No.12, 2020, pp. 2660-2670.
• Y.-E. Lee, H. M. Husin, M.-P. Forte, S.-W. Lee, and K. J. Kuchenbecker, “Learning to Estimate Palpation Forces in Robotic Surgery from Visual-Inertial Data,” IEEE Transactions on Medical Robotics and Bionics, Vol. 5, No. 3, 2023, pp. 496-506.
• 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.

Publication

Journals

• Y.-E. Lee, H. M. Husin, M.-P. Forte, S.-W. Lee, and K. J. Kuchenbecker, “Learning to Estimate Palpation Forces in Robotic Surgery from Visual-Inertial Data,” IEEE Transactions on Medical Robotics and Bionics, Vol. 5, No. 3, 2023, pp. 496-506.
• J.-H. Jeong, J.-H. Cho, Y.-E. Lee, S.-H. Lee, G.-H. Shin, Y.-S. Kweon, J. R. Millán, K.-R. Müller, and S.-W. Lee, “2020 International Brain-Computer Interface Competition: A Review,” Frontiers in Human Neuroscience, Vol. 16, 2022, pp. 898300.
• 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. 315, 2021.
• Y.-E. Lee, N.-S. Kwak, S.-W. Lee, “A Real-time movement artifact removal method for ambulatory brain-computer interfaces,” IEEE Trans. on Neural Systems & Rehabilitation Engineering, Vol. 28, No.12, 2020, pp. 2660-2670.
• 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.
• 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.
• J.-H. Jeong, J.-H. Cho, K.-H. Shim, Y.-E. Lee, and S.-W. Lee, “NeuroBoT: Short-Term Brain-Machine Learning of Real-Time EEG Decoding for BMI-of-Things,” Arxiv, 2025.
• S. J. Madsen*, Y.-E. Lee*, L. Q. Uddin, J. A. Mumford, D. M. Barch, D. A. Fair, I. H. Gotlib, R. A. Poldrack, A. Kuceyeski, M. Saggar, “Predicting Task Activation Maps from Resting-State Functional Connectivity using Deep Learning,” bioRxiv, 2025.

Conferences

• D.-S. Kim, S.-H. Lee, Y.-E. Lee, S.-W. Lee, “Towards Speech Synthesis of Unconstrained Sentences from Speech-Related Biosignals,” Proc. of EMBC, 2024.
• Y.-E. Lee, S.-H. Lee, S. Kim, J.-S. Lee, D.-S. Kim, S.-W. Lee, “Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals,” Proc. of 12th IEEE International Winter Conference on Brain-Computer Interface, Feb. 26-28, 2024.
• S. Kim, S.-H. Lee, Y.-E. Lee, J.-W. Lee, J.-H. Park, P. Kazanzides, S.-W. Lee, “Brain-Driven Representation Learning Based on Diffusion Model,” Proc. of 12th IEEE International Winter Conference on Brain-Computer Interface, Feb. 26-28, 2024.
• S.-H. Lee, Y.-E. Lee, S. Kim, B.-K. Ko, J.-Y. Kim, S.-W. Lee, “Neural Speech Embeddings for Speech Synthesis Based on Deep Generative Networks,” Proc. of 12th IEEE International Winter Conference on Brain-Computer Interface, Feb. 26-28, 2024.
• Y.-E. Lee, S.-H. Lee, S.-H Kim, and S.-W. Lee, “Towards Voice Reconstruction from EEG during Imagined Speech,” AAAI Conference on Artificial Intelligence (AAAI), 2023.
• S. Kim, Y.-E. Lee, S.-H. Lee, and S.-W. Lee, “Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG,” Proc. of Interspeech, 2023.
• J. Lee, S.-H. Lee, Y.-E. Lee, S. Kim, S.-W. Lee, “Sentence Reconstruction Leveraging Contextual Meaning from Speech-Related Brain Signals,” 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2023.
• Y.-E. Lee, S.-H. Lee, S. Kim, S.-H. Kim, J.-S. Lee, and S.-W. Lee, “Speech Synthesis from Brain Signals Based on Generative Model,” Proc. 11th IEEE International Winter Conference on Brain-Computer Interface, Feb. 20-22, 2023.
• S. Kim, J.-W. Lee, Y.-E. Lee, S.-H. Lee, “Subject-Independent Classification of Brain Signals using Skip Connections,” Proc. 11th IEEE International Winter Conference on Brain-Computer Interface, Feb. 20-22, 2023.
• S.-H. Lee, Y.-E. Lee, S. Kim, S.-W. Lee, “Towards Neural Decoding of Imagined Speech based on Spoken Speech,” Proc. 11th IEEE International Winter Conference on Brain-Computer Interface, Feb. 20-22, 2023.
• S. Kim, Y.-E. Lee, S.-H. Lee, S.-W. Lee, “Speech EEG classification based on residual learning for user-independent brain-computer interfaces,” Winter Symposium of the Korean Society of Brain Engineering, 2023.
• Y.-E. Lee and S.-H. Lee, “EEG-Transformer: Self-attention from Transformer Architecture for Decoding EEG of Imagined Speech,” Proc. 10th IEEE International Winter Conference on Brain-Computer Interface, Feb. 21-23, 2022.
• S.-H. Lee, Y.-E. Lee and S.-W. Lee, “Toward Imagined Speech based Smart Communication System: Potential Applications on Metaverse Conditions,” Proc. 10th IEEE International Winter Conference on Brain-Computer Interface, Feb. 21-23, 2022.
• Y.-E. Lee and S.-W. Lee, “Decoding event-related potential from ear-EEG signals based on ensemble convolutional neural networks in ambulatory environment,” Proc. 9th IEEE International Winter Conference on Brain-Computer Interface, Feb. 22-24, 2021.
• S.-H. Lee*, Y.-E. Lee*, S.-W. Lee, “Voice of your brain: Cognitive representations of imagined speech, overt speech, and speech perception based on EEG,” Arxiv preprint, 2021.
• Y.-E. Lee and M. Lee, “Decoding visual responses based on deep neural networks with ear-EEG signals,” Proc. 8th IEEE International Winter Conference on Brain-Computer Interface, Feb. 26-28, 2020.
• Y.-E. Lee, M. Lee, S.-W. Lee, “Reconstructing ERP signals using generative adversarial networks for mobile brain-machine interface,” Arxiv preprint, 2020.
• Y.-E. Lee, H. M. Husin, M. P. Forte, S.-W. Lee, K. J. Kuchenbecker, “Vision-based Force Estimation for a da Vinci Instrument Using Deep Neural Networks,” SAGES, 2020.

Patents

• “User Authentication Apparatus and Method Using Brainwave Signal According to Imagined Speech,” Register Number: 12,204,624, (U.S.A).
• “Method, Device and Program for Learning Artificial Neural Networks Based on Speech Imagination Biosignals and Phoneme Information,” Application Number: US 18/981,333 (U.S.A).
• “Method and Apparatus for Speech Synthesis Based on Brain Signals During Imagined Speech,” Application Number: US 18/199,670 (U.S.A).
• “Brain-Computer Interface System for Supporting New Language Acquisition Using Brain Signals,” Register Number: 10-2766793 (Korea).
• “Method and apparatus for speech synthesis based on brain signals during imagined speech,” Register Number: 10-2709425 (Korea).
• “Personal authentication apparatus and system based on brain signals of imagined speech,” Register Number: 10-2522391 (Korea).
• “Apparatus and Method for Left- and Right-Foot Classification Based on Brain Signals,” Registration Number: 10-2276991 (Korea).
• “Smart Home Control System and Apparatus based on Brain-Computer Interface Using Minimum Stimulus,” Application Number: 10-2022-0163784 (Korea).
• “A method for user intent recognition through similarity comparison by extracting features from imagined speech and overt speech in brain-computer interface,” Application Number: 10-2022-0163782 (Korea).
• “Brain-Computer Interface System for Supporting New Language Acquisition Using Brain Signals,” Application Number: 10-2022-0173553 (Korea).
• “Bio-signal-based metaverse system implementation method for predicting and reconstructing user’s facial emotions and mouth shapes based on bio-signals,” Application Number: 10-2022-0163781 (Korea).
• “Personal authentication apparatus and system based on brain signals of imagined speech,” Application Number: 10-2021-0079992 (Korea).
• “Apparatus and method for left- and right-foot classification based on brain signals,” Application Number: 10-2018-0135194 (Korea).

Peer Review

• AAAI
• IEEE Transactions on Neural Networks and Learning Systems
• IEEE Transactions on Industrial Informatics
• IEEE Transactions on Instrumentation & Measurement
• Journal of Engineering in Medicine
• Computer Methods and Programs in Biomedicine
• Scientific Data
• Journal of Integrative Neuroscience
• Brain Connectivity
• Measurement Science Review
• IEEE International Conference on Systems, Man, and Cybernetics (SMC)
• IEEE International Winter Conference on Brain-Computer
• International Conference on Multimodal Interaction

Skills

• Programming: Python, PyTorch, TensorFlow, C/C++, MATLAB
• ML Models: CNNs, RNNs (LSTM, GRU), Transformers, GANs, Diffusion Models, AutoEncoders
• Techniques: Real-time decoding, Generative modeling, Few-shot Learning, Signal processing

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