Latest Publications

A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback

We presented a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE)-based motion artifact correction, and fast 3D image reconstruction using a pre-calculated Jacobian. Trained on high-density DOT data, the system effectively processes ~750 channels with low latency, outperforming traditional methods and enabling reliable, real-time brain monitoring for BCI and neurofeedback applications in movement-intensive contexts.

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Simultaneous Mental Fatigue and Mental Workload Assessment With Wearable High-Density Diffuse Optical Tomography

In this study, we utilised high-density diffuse optical tomography (HD-DOT) to improve the assessment of mental workload and fatigue by enabling high-resolution 3D brain imaging. Using machine learning, subject-specific classification achieved up to 97.93% accuracy for cognitive tasks, demonstrating HD-DOT’s potential to enhance precision and adaptability in BCI applications.

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AI-Enabled Piezoelectric Wearable for Joint Torque Monitoring

We presented an AI-enabled, wearable joint torque monitoring device based on piezoelectric boron nitride nanotubes (BNNTs), designed for real-time, accurate, and low-cost assessment of knee biomechanics. By combining biomechanically tailored materials with on-device neural networks, the system enables precise torque estimation and energy harvesting, offering a scalable solution for joint health monitoring and rehabilitation across diverse global settings.

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Selected Publications

Y. Xia et al., "A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 33, pp. 1220-1230, 2025.

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J. Chen et al., "Simultaneous Mental Fatigue and Mental Workload Assessment With Wearable High-Density Diffuse Optical Tomography", in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 33, pp. 1242-1251, 2025.

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Y. Xia, K. Wang et al., and Zhao H, "Low-cost, smartphone-based instant three-dimensional registration system for infant functional near-infrared spectroscopy applications", Neurophotonics, 2023.

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J. Chang, J. Li, J. Ye et al. "AI-Enabled Piezoelectric Wearable for Joint Torque Monitoring", Nano-Micro Lett (IF36.3). 17, 247, 2025.

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H. Zhao, S. Brigadoi et al., "A wide field-of-view, modular, high-density diffuse optical tomography system for minimally constrained three-dimensional functional neuroimaging", Biomedical Optics Express, 2020. (Editorial Pick, and one of Top Downloads of the Year).

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H. Zhao, A. Soltan et al., "A scalable optoelectronic neural probe architecture with self-diagnostic capability", IEEE Transactions on Circuits and Systems I: Regular Papers, 2018 (Best Paper of BioCAS).

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Publications List

Y. Xia et al., "A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 33, pp. 1220-1230, 2025.
J. Chen et al., "Simultaneous Mental Fatigue and Mental Workload Assessment With Wearable High-Density Diffuse Optical Tomography," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 33, pp. 1242-1251, 2025.
J. Li et al., "Real-Time Motion Artifact Removal in fNIRS with Denoising Autoencoder at the Edge," IEEE International Symposium on Circuits and Systems, 2025.
S. Guan, Y. Li, Y. Gao, Y. Luo, H. Zhao, D. Yang, R. Li, "Continuous Wave-Diffuse Optical Tomography (CW-DOT) in Human Brain Mapping: A Review." Sensors 2025(7), 2040, 2025.
Q. He, Y. Xia et al., "Reconfigurable hardware-accelerated, multi-channel, adaptive temperature control platform of VCSELs for high-density fNIRS/DOT," Biomed. Opt. Express 16, 2601-2614, 2025
J. Chang, J. Li, J. Ye et al., "AI-Enabled Piezoelectric Wearable for Joint Torque Monitoring," Nano-Micro Lett. 17, 247, 2025.
J. Chen et al., "A Multimodal fNIRS-EEG BCI System for Mental Monitoring of Disabled Wheelchair Athletes," SPIE Photonic West 2024.
J. Chen et al., "An AI-empowered, fNIRS-EEG BCI for Mental State Classification," fNIRS 2024.
A. Thomas, J. Chen, et al., “High stimuli virtual reality training for a brain-controlled robotic wheelchair,” IEEE RAS International Conference on Robotics and Automation (ICRA), 2024.
J. Chen, et al., “Mental Fatigue Evaluation With fNIRS/DOT: A Feasibility Study,” IEEE Engineering in Medicine and Biology Conference (EMBC), 2024.
L. Zhu, J. Chen (co-first author), et al., “Wearable Near-Eye Tracking Technologies for Health: A Review,” Bioengineering, 2024.
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), vol. 32, no. 4, pp. 763–773, 2024.
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.
S. Gao et al., “Temporal Dynamics and Physical Priori Multimodal Network for Rehabilitation Physical Training Evaluation,” IEEE Journal of Biomedical and Health Informatics, pp. 1–11, Jun. 2024.
S. Wang et al., ‘Memristor-based adaptive neuromorphic perception in unstructured environments’, Nat. Commun., vol. 15, no. 1, p. 4671, 2024.
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, pp. 1–11, Jan. 2024.
J. Chen et al., “fNIRS-EEG BCIs for Motor Rehabilitation: A Review,” Bioengineering, vol. 10, no. 12, pp. 1393–1393, Dec. 2023.
Y. Xu, H. Zhao, and Cosimo Ieracitano, “Editorial: Advances in brain-computer interface technologies for closed-loop neuromodulation,” Frontiers in Neuroscience, vol. 17, Nov. 2023.
Y. Zhao et al., “FPL Demo: A Learning-Based Motion Artefact Detector for Heterogeneous Platforms,” 2023 33rd International Conference on Field-Programmable Logic and Applications (FPL), Sep. 2023.
Y. Xia et al., “Low-cost, smartphone-based instant three-dimensional registration system for infant functional near-infrared spectroscopy applications,” Neurophotonics, vol. 10, no. 04, Oct. 2023.
X. Zhou et al., “Review of recent advances in frequency-domain near-infrared spectroscopy technologies [Invited],” Biomedical Optics Express, vol. 14, no. 7, pp. 3234–3234, Jun. 2023.
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, Jan. 2023.
Y. Zhao et al., “Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset,” ACM International Conference Proceeding Series, 2023.
Y. Zhao et al., “Learning based motion artifacts processing in fNIRS: a mini review,” Frontiers in Neuroscience, vol. 17, Nov. 2023.
Y. Wu et al., “Editorial: Wearable and Implantable Electronics for the next Generation of Human-Machine Interactive Devices,” Frontiers in electronics, vol. 3, Jun. 2022.
H. Zhao et al., "ANIMATE: wearable, flexible, and ultra-lightweight high-density diffuse optical tomography technologies for functional neuroimaging of newborns", European Conferences on Biomedical Optics(ECBO), Proc. SPIE 11920, Diffuse Optical Spectroscopy and Imaging VIII, 119201A, 2021.
E. E. Vidal-Rosas et al., “Evaluating a new generation of wearable high-density diffuse optical tomography technology via retinotopic mapping of the adult visual cortex,” Neurophotonics, vol. 8, no. 02, Apr. 2021.
Elisabetta Maria Frijia et al., “Towards cot-side mapping of the sensorimotor cortex in preterm and term infants with wearable high-density diffuse optical tomography,” European Conferences on Biomedical Optics 2021 (ECBO), Dec. 2021.
H. Zhao et al., “Design and validation of a mechanically flexible and ultra-lightweight high-density diffuse optical tomography system for functional neuroimaging of newborns,” Neurophotonics, vol. 8, no. 01, Mar. 2021.
J. Uchitel et al., “Wearable, Integrated EEG–fNIRS Technologies: A Review,” Sensors, vol. 21, no. 18, pp. 6106–6106, Sep. 2021.
E. E. Vidal-Rosas et al., “Evaluating a new generation of wearable high-density diffuse optical tomography technology via retinotopic mapping of the adult visual cortex,” Neurophotonics, vol. 8, no. 02, Apr. 2021.
E. E. Vidal-Rosas et al., “Wearable high-density diffuse optical tomography (HDDOT) for unrestricted 3D functional neuroimaging,” Optical Techniques in Neurosurgery, Neurophotonics, and Optogenetics, Mar. 2021.
H. Zhao et al., “A wide field-of-view, modular, high-density diffuse optical tomography system for minimally constrained three-dimensional functional neuroimaging,” Biomedical Optics Express, vol. 11, no. 8, pp. 4110–4110, Jul. 2020.
H. Zhao (2022). Optogenetic Implants. In: Sawan, M. (eds) Handbook of Biochips. Springer, New York, NY.H.
H. Zhao et al., “Advances in wearable high-density diffuse optical tomography: first applications of a new commercial technology and development of an infant-specific research,” Diffuse Optical Spectroscopy and Imaging VII, Jul. 2019.
S. Brigadoi et al., “Integrating motion sensing and wearable, modular high-density diffuse optical tomography: Preliminary results,” Diffuse Optical Spectroscopy and Imaging VII, Jul. 2019.
H. Zhao et al., “A Scalable Optoelectronic Neural Probe Architecture with Self-Diagnostic Capability,” IEEE Transactions on Circuits and Systems I-regular Papers, vol. 65, no. 8, pp. 2431–2442, Aug. 2018.
R. Ramezani et al., “On-Probe Neural Interface ASIC for Combined Electrical Recording and Optogenetic Stimulation,” IEEE Transactions on Biomedical Circuits and Systems, vol. 12, no. 3, pp. 576–588, Jun. 2018.
H. Zhao and R. J. Cooper, “Review of recent progress toward a fiberless, whole-scalp diffuse optical tomography system,” Neurophotonics, vol. 5, no. 01, pp. 1–1, Sep. 2017.
H. Zhao, “Recent Progress of Development of Optogenetic Implantable Neural Probes,” International Journal of Molecular Sciences, vol. 18, no. 8, pp. 1751–1751, Aug. 2017.
R. Cooper et al., “The μNTS: a wearable, modular, high-density diffuse optical tomography,” European Conference on Biomedical Optics 2017(ECBO), 2017.
H. Zhao et al., “A CMOS-based neural implantable optrode for optogenetic stimulation and electrical recording,” 2015 IEEE Biomedical Circuits and Systems (BIOCAS), Oct. 2015.
F. Dehkhoda et al., “Smart optrode for neural stimulation and sensing,” Spiral (Imperial College London), 2015 IEEE SENSORS, Nov. 2015.
H. Zhao, D. Sokolov, and P. Degenaar, “An implantable optrode with Self-diagnostic function in 0.35µm CMOS for optical neural stimulation,” 2014 IEEE Biomedical Circuits and Systems (BIOCAS), Oct. 2014.
A. Soltan et al., “An 8100 pixel optoelectronic array for optogenetic retinal prosthesis,” 2014 IEEE Biomedical Circuits and Systems (BIOCAS), Oct. 2014.