Kyuhwa aims to discover informative patterns from brain signals using a data-driven approach to gain a deeper understanding of brain mechanisms in order to help motor-impaired patients regain their motor functionalities using personalized real-time neurofeedback technologies.
Dr Kyuhwa Lee is a machine learning scientist with over five years of experience in brain signal decoding and over ten years of applied machine learning research. Having an interdisciplinary background in computer science, electrical engineering and neuroscience, Kyuhwa plays roles in designing and applying real-time machine learning algorithms for analyzing and decoding brain signal patterns of patients.
His aim is to help motor-impaired patients regain their motor functionalities using personalised real-time neurofeedback technologies.
Kyuhwa has experience in developing real-time closed-loop brain signal decoding algorithms for patients implanted with epidural and subdural electrocorticography (ECoG), Utah microelectrode arrays, deep brain stimulation (DBS) and also with non-invasive electroencephalography (EEG).
Prior to joining Wyss Center, Kyuhwa obtained his PhD degree at Imperial College London and did further research in clinical environments at Lausanne University Hospital (CHUV) and Swiss Federal Institute of Technology (EPFL).