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About

Zhi-Hao Kang is a pioneering researcher in the field of brain-computer interfaces (BCIs) and rehabilitation robotics, dedicated to restoring movement and independence for individuals with severe motor disabilities. His core research focuses on developing intelligent systems that translate neural signals into commands for robotic rehabilitation devices, bridging the gap between the human brain and assistive technology. Kang’s major contributions include the creation of a brain-controlled rehabilitation system (BCRS) that leverages multiple kernel learning for robust signal interpretation, and the integration of a P300 speller with an elastic mechanism for a rehabilitation robot. His work on automatic feature extraction using Independent Component Analysis combined with multiple kernel learning has further refined the usability of these systems, reducing the need for constant caregiver assistance. With his most-cited papers accumulating over 15 citations, Kang’s research is foundational in advancing non-invasive, patient-driven rehabilitation. Notably, his development of a combined feature set to assess user independence offers a practical metric for tracking recovery progress, making his work a vital resource for students and researchers exploring the intersection of machine learning, neuroscience, and robotics in clinical rehabilitation.