Papers
3
Total Citations
105
H-Index
3
About
Yuxia Hu’s research career bridges two transformative fields: advanced manufacturing robotics and neurorehabilitation via brain–computer interfaces (BCIs). Her early foundational work in robotic machining, particularly the 1999 paper on sculptured surface cutting, established key algorithms for rough machining that remain cited today. In recent years, Hu has pioneered the application of deep learning to decode single upper limb motor imagery from EEG signals. Her 2023 work on a multi-branch fusion convolutional neural network achieved 24 citations by addressing a critical gap—most MI-BCI systems focus on bilateral tasks, yet many patients require rehabilitation of one limb. Hu further integrated these advances into a hybrid BCI system for active and passive upper limb training, enabling patients with craniocerebral injuries to engage volitionally in their recovery. This work, though recent, demonstrates her commitment to translating neural decoding into practical rehabilitation tools. With a career spanning from precision manufacturing to neural engineering, Hu exemplifies how robotics and AI can converge to restore human function. Her research not only advances fundamental understanding of motor imagery but also directly impacts clinical neurorehabilitation.
Research Focus
Key Achievements
Top Papers
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