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About

Dr. Xiuling Liu is a pioneering researcher at the intersection of neural engineering, soft robotics, and intelligent control systems. Her work primarily focuses on decoding human intent through advanced brain-computer interfaces (BCIs) and surface electromyography (sEMG) for rehabilitation and prosthetic applications. Dr. Liu’s most impactful contribution is her 2021 study on motor imagery EEG classification, which introduced a multiscale space-time-frequency feature-guided multitask learning CNN—a breakthrough that has garnered 51 citations for its ability to accurately decode neural activities. She further advanced this field with a 3D convolutional neural network that integrates multiscale spatial and temporal cues, achieving 14 citations. In parallel, Dr. Liu has made significant strides in soft robotics, developing highly stretchable and self-adhesive elastomers for high-performance strain sensors (26 citations), which enhance the mechanical properties of PDMS. Her work on real-time control of intelligent prosthetic hands using improved temporal convolutional networks (6 citations) and continuous joint angle estimation via SE-TCN networks (9 citations) demonstrates her commitment to practical, human-centered robotics. With a foundation in dissipativity-based robust control for robot manipulators, Dr. Liu’s interdisciplinary research bridges theoretical control theory with tangible assistive technologies, making her a key figure in advancing human-machine interaction.