Yunjia Xia
Yunjia Xia is a PhD student at HUB of Intelligent Neuro-Engineering (HUBIN) of the Division of Surgery and Interventional Science at the University College London (UCL). He is also a member of DOT-HUB at the UCL. His project is mainly focused on AI-supported wearable brain imaging and brain computer interface. Prior to his PhD study, he received his MRes and BEng degrees from the UCL-University of Cambridge, United Kingdom, and Beihang University, China, respectively.
Research Interests
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FPGA Development and Application in Wearable and Interventional Medical Technologies
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AI Hardware Optimization for Enhanced Functional Brain Imaging
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Brain-Computer Interface (BCI) for Rehabilitation and Healthcare Monitoring
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Advanced Electronics Integration in Medical Devices for Improved Healthcare Solutions
Publications
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Y. Xia et al., "A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 33, pp. 1220-1230, 2025.
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Y. Xia et al., "An FPGA-based, multi-channel, real-time, motion artifact detection technique for fNIRS/DOT systems," IEEE International Symposium on Circuits and Systems (ISCAS), 32(4), 763-773, 2025.
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R. Ercan, Y. Xia et al., "A real-time machine learning module for motion artifact detection in fNIRS," IEEE International Symposium on Circuits and Systems (ISCAS), 2024.
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R. Ercan, Y. Xia et al., "An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection," IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2024.
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Y. Xia et al., "A low-cost, smartphone-based instant 3D scanning system for infant fNIRS applications," Neurophotonics, 10(4):046601, 2023.
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J. Chen, Y. Xia et al., "fNIRS-EEG brain computer interface for rehabilitation," Bioeigneering, 10(12), 2023.
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X. Zhou, Y. Xia et al., "Review of recent advances in frequency-domain near-infrared spectroscopy technologies," Biomedical Optics Express, 14(7), 3234-3258, 2023.
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Y. Xia et al., "A remote-control, smartphone-based automatic 3D scanning system for fNIRS/DOT applications," Optica Biophotonics Congress: Optics in the Life Sciences, 2023.
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Y. Zhao, Y. Xia et al., "Edge acceleration for machine learning-based motion artifact detection on fNIRS dataset," The 11th IWOCL, 2023.