Sangwoo Park

About

Sangwoo Park is a leading researcher in the intersection of brain-computer interfaces (BCI) and rehabilitation robotics, with a primary focus on decoding human intent from neural signals. His most cited work, "EEG-based Gait State and Gait Intention Recognition Using Spatio-Spectral Convolutional Neural Network" (2019, 17 citations), tackles a critical challenge in lower limb exoskeleton control: the trade-off between classification accuracy and system responsiveness. Park’s key contribution lies in developing a spatio-spectral convolutional neural network that simultaneously extracts spatial and spectral features from EEG data, enabling real-time, high-accuracy recognition of gait states (walking vs. standing) and gait intention. This work bridges the gap between machine learning decoders and practical neuroprosthetic applications, demonstrating that deep learning can resolve the longstanding accuracy-latency dilemma in BCI-driven exoskeletons. By advancing EEG-based intention decoding, Park has laid foundational groundwork for more intuitive, responsive assistive devices, directly impacting the fields of neural engineering and human-robot interaction. His research continues to push the boundaries of how neural signals can be harnessed for seamless, real-world control of wearable robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   EEG-based Gait State and Gait Intention Recognition Using Spatio-Spectral Convolutional Neural Network

   17 citations · 2019

Key Collaborators

Contact & Links