16:00 - 18:00
Dr. Kyuhwa Lee
Computer Science and Electronic Engineering, School of
Dr. Kyuhwa Lee is a post-doctoral researcher at EPFL (Swiss Federal Institute of Technology in Lausanne) with a focus on machine learning methods for analyzing and decoding brain signals and their applications in real life. He was advised by Dr. Yiannis Demiris and Dr. Tae-Kyun Kim to obtain his Ph.D. at Imperial College London, and by Dr. Aaron Bobick to obtain his Master's degree at Georgia Institute of Technology.
Closed-loop brain-machine interfaces (BMI) provide a mutual learning paradigm where a machine learns how to recognize a user's brain states while the user also adapts to the decoder feedback of the machine to improve the overall performance. In this talk, we present some BMI that concerns the detection of a user's intention to move his/her own upper or lower limbs as well as error-related signals which occurs when a user detects a mismatch between the observation and expectation. Finally, we show a rehabilitation application of a chronic paraplegic user who has shown improvements in motor and sensor functions after trained with BMI.