Myoungho Lee

About

Myoungho Lee is a researcher whose work sits at the intersection of neuroscience and machine learning, with a primary focus on brain-machine interfaces (BMI). His key contributions involve decoding neural signals to control external devices, a field critical for developing assistive technologies. Lee’s most cited work, “Classification of BMI control commands from rat's neural signals using extreme learning machine” (2009, 10 citations), pioneered the application of Extreme Learning Machines (ELM) to classify movement commands from spike trains recorded in the CA1 hippocampus of rats. By analyzing ensembles of 34 simultaneously recorded neurons during a target-to-goal task, he demonstrated that ELM could efficiently translate neural activity into machine control commands. This approach offered a faster, more computationally efficient alternative to traditional neural decoding methods. His earlier 2008 paper (2 citations) laid the groundwork for this methodology. While his citation counts are modest, Lee’s work represents an early and innovative step in applying advanced machine learning to real-time neural decoding, contributing to the broader goal of creating seamless, responsive brain-machine interfaces for motor control and rehabilitation.

Research Focus

Key Achievements

Top Papers

1. 1
   Classification of BMI control commands from rat's neural signals using extreme learning machine

   10 citations · 2009
2. 2
   Classification of BMI Control Commands Using Extreme Learning Machine from Spike Trains of Simultaneously Recorded 34 CA1 Single Neural Signals

   2 citations · 2008

Key Collaborators

Contact & Links