Sicong Zhang
Papers
12
Total Citations
262
H-Index
6
About
Sicong Zhang is a biomedical engineer and neurotechnology researcher whose work spans brain-computer interfaces (BCIs), rehabilitation robotics, and neural signal processing. His most influential contribution, a 2020 paper on data augmentation for motor imagery signal classification using hybrid neural networks (122 citations), significantly advanced the field of spontaneous BCIs, addressing the critical challenge of limited training data in EEG-based systems. Building on this foundation, Zhang demonstrated real-world clinical impact through a 2022 randomized controlled trial showing that BCI-controlled robot training meaningfully improves outcomes in subacute stroke patients (44 citations). His research extends into the biomechanics of assistive devices, including novel arthropod-inspired joint models, topology-optimized finger exoskeletons, and sEMG-driven adaptive impedance control for upper limb rehabilitation robots — collectively advancing the design and control of next-generation assistive technologies. More recently, Zhang has explored EEG-based emotion recognition, examining how music tempo modulates neural emotional states (28 citations). With over 250 total citations, his interdisciplinary portfolio bridges neuroscience, robotics, and clinical rehabilitation, making his work highly relevant for researchers developing intelligent, human-centered assistive and therapeutic systems.
Research Focus
Key Achievements
Top Papers
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- 3Music tempo modulates emotional states as revealed through EEG insights28 citations · 2025
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- 9Design of rigid-compliant parallel exoskeleton knee joint4 citations · 2017
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