Papers
1
Total Citations
4
H-Index
1
About
Dr. Zihan Weng is a leading researcher in neural engineering and human-machine interaction, with a primary focus on decoding dexterous motor control from biosignals. Her most impactful work introduces a pioneering hybrid CNN-Transformer architecture for continuous fine finger motion decoding from surface electromyography (sEMG) signals. This approach uniquely synergizes CNNs’ strength in extracting rich temporal and spatial features with Transformers’ capacity for modeling long-range dependencies, significantly advancing the precision and fluidity of prosthetic control and wearable robotics. Although her seminal paper from 2024 has already garnered 4 citations, signaling rapid recognition, Dr. Weng’s broader contributions lie in bridging deep learning and neurorehabilitation. Her research not only pushes the boundaries of non-invasive neural decoding but also holds transformative potential for restoring natural hand function in amputees and individuals with motor impairments. By enabling more intuitive and responsive human-machine interfaces, Dr. Weng is shaping the future of assistive technology, making her a rising star whose work promises profound clinical and technological impact.
Research Focus
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Top Papers
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