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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. 

Email: yunjia.xia.18@ucl.ac.uk

Research Interests

  • FPGA Development and Application in Wearable and Interventional Medical Technologies

  • AI Hardware Optimization for Enhanced Functional Brain Imaging

  • Brain-Computer Interface (BCI) for Rehabilitation and Healthcare Monitoring

  • Advanced Electronics Integration in Medical Devices for Improved Healthcare Solutions​

Publications

  • Xia, Y. et al. (2024). 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.

  • Ercan, R., Xia, Y. et al. (2024). A real-time machine learning module for motion artifact detection in fNIRS. IEEE International Symposium on Circuits and Systems (ISCAS)

  • Ercan, R., Xia, Y. et al. (2024). An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection. IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

  • Xia, Y. et al. (2023). A low-cost, smartphone-based instant 3D scanning system for infant fNIRS applications. Neurophotonics, 10(4):046601.

  • Chen, J., Xia, Y. et al. (2023). fNIRS-EEG brain computer interface for rehabilitation. Bioeigneering, 10(12).

  • Zhou, X., Xia, Y. et al. (2023). Review of recent advances in frequency-domain near-infrared spectroscopy technologies. Biomedical Optics Express, 14(7), 3234-3258.

  • Xia, Y. et al. (2023). A remote-control, smartphone-based automatic 3D scanning system for fNIRS/DOT applications. Optica Biophotonics Congress: Optics in the Life Sciences.

  • Zhao, Y., Xia, Y. et al. (2023). Edge acceleration for machine learning-based motion artifact detection on fNIRS dataset. The 11th IWOCL.

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