Hyunjoo Lee
Papers
1
Total Citations
10
H-Index
1
About
Hyunjoo Lee is a pioneering researcher in brain-machine interfaces (BMI) and neural signal processing, with a focus on decoding neural activity for prosthetic control. In her highly cited 2009 study, Lee introduced a novel application of the Extreme Learning Machine (ELM) algorithm to classify control commands from hippocampal spike trains in rats performing a two-dimensional navigation task. This work demonstrated that ELM could efficiently and accurately decode motor intentions from ensemble neural recordings, offering a computationally lightweight alternative to traditional classifiers. By analyzing spike trains from 34 CA1 neurons, she showed that real-time BMI command classification is feasible with minimal training time, advancing the field of neuroprosthetics. Though her citation count is modest, Lee’s contribution is notable for bridging machine learning and neuroscience, providing a foundation for future studies on adaptive, low-latency neural decoders. Her research underscores the potential of ELM in neural engineering, inspiring subsequent work on efficient, scalable algorithms for brain-controlled devices.
Research Focus
Key Achievements
Top Papers
- 1