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About

Kun Chen is a leading researcher at the intersection of brain-computer interfaces (BCI), rehabilitation robotics, and human-robot interaction. His work focuses on decoding motor imagery (MI) electroencephalography (EEG) signals to create intuitive control systems for assistive and rehabilitative technologies. Chen’s most impactful contribution is a novel method for feature extraction of four-class motor imagery EEG signals using functional brain networks, published in 2019, which has garnered 91 citations and addresses the critical challenge of improving classification accuracy in multi-class BCI systems. He has further advanced the field by developing flexible coding schemes for robotic arm control driven by MI decoding, and by pioneering active interaction control for rehabilitation robots that combines motion recognition with adaptive impedance control—work that enables seamless patient-driven therapy. Chen’s research extends to brain-robot shared control systems, cooperative ankle rehabilitation robots, and even automated 3D surface imaging, demonstrating a broad technical range. His notable achievements include integrating Huffman coding for multi-degree-of-freedom robot control and developing a NAO robot walking control system driven solely by motor imagery, pushing the boundaries of how paralyzed and stroke patients can interface with and control external devices.