About me

Postdoctoral Researcher, Stanford University. Brain-Computer Interfaces & Neural Decoding.

I am a postdoctoral researcher in the Department of Psychiatry and Behavioral Sciences at Stanford University. My research is in brain-computer interfaces and neural decoding: I build machine learning models that read neural signals (EEG, fMRI, and intracranial recordings) to reconstruct imagined speech and movement.

Before Stanford, I completed my Ph.D. in Brain and Cognitive Engineering at Korea University, advised by Seong-Whan Lee. My work spans real-time decoding, generative models (GANs and diffusion), and multimodal signal processing for BCI. I am a co-author of the OpenBMI toolbox, a widely used open BCI dataset, and I hold several U.S. and Korean patents on imagined-speech based interfaces.

Feel free to reach out about brain signal decoding, speech neuroprosthetics, or generative models for neural data.

selected publications

  1. HBM
    Efficient Deep Learning Models for Predicting Individualized Task Activation from Resting-State Functional Connectivity
    S. J. Madsen*, Young-Eun Lee*, S. Quah, and 8 more authors
    Human Brain Mapping, 2026
  2. AAAI
    Towards Voice Reconstruction from EEG during Imagined Speech
    Young-Eun Lee, S.-H. Lee, S.-H. Kim, and 1 more author
    In AAAI Conference on Artificial Intelligence, 2023
  3. TMRB
    Learning to Estimate Palpation Forces in Robotic Surgery from Visual-Inertial Data
    Young-Eun Lee, H. M. Husin, M.-P. Forte, and 2 more authors
    IEEE Transactions on Medical Robotics and Bionics, 2023
  4. TNSRE
    A Real-time movement artifact removal method for ambulatory brain-computer interfaces
    Young-Eun Lee, N.-S. Kwak, and S.-W. Lee
    IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020
  5. GigaScience
    EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms: An Investigation into BCI Illiteracy
    M.-H. Lee, O.-Y. Kwon, Y.-J. Kim, and 5 more authors
    GigaScience, 2019