Latest Publications

Low-cost, smartphone-based instant three-dimensional registration system for infant functional near-infrared spectroscopy applications

We presented a smartphone-based registration system for functional near-infrared spectroscopy (fNIRS)/diffuse optical tomography (DOT), aimed at instantly scanning a 6-month-old infant's head. This remotely controlled system achieved a 2-second full-head 3D scan, with an average optode positioning error of 1.8 mm on a static infant head model, suggesting its potential for accurate and near-instant fNIRS/DOT spatial registration.

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Memristor-based adaptive neuromorphic perception in unstructured environments

In this paper, we present a memristor-based differential neuromorphic computing and perceptual signal processing method for robotic and autonomous systems. It enhances real-time adaptation to unstructured environments, achieving safe robotic grasping and high-accuracy decision-making in autonomous driving with up to 94% accuracy.

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An ultralow-power, real-time machine learning based fNIRS
motion artefacts detection

We introduce an ultralow-power, real-time machine learning module for motion artifact detection in fNIRS systems, achieving 97.42% accuracy with low FPGA resource and power usage. Outperforming conventional CPU SVM methods, this study demonstrates the feasibility of high-accuracy, low-power FPGA-based classifiers for wearable devices with limited resources.

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Temporal Dynamics and Physical Priori Multimodal Network for Rehabilitation Physical Training Evaluation

We introduced an advanced sensor-based rehabilitation assessment method that captures subtle movements missed by traditional techniques. Our multimodal algorithm, integrating surface electromyography (sEMG) and stress signals, achieved 94.7% accuracy in tests with 24 subjects, outperforming conventional methods. This model enhances real-time and home-based physical training through improved bioinformatic feature analysis.

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Selected Publications

Xia Y, Wang K, 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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Zhao H, Brigadoi S, 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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Ercan R, Xia Y, et al., and Zhao H, "An ultralow-power, real-time machine learning based fNIRS motion artefacts detections", IEEE Transactions on Very Large Scale Integration Systems, 2024.

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Zhou X, Xia Y, et al., and Zhao H, "Review of recent advances in frequency-domain near-infrared spectroscopy technologies", Biomedical Optics Express, 2023 (Invited Review).

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Zhao H, Soltan A, 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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Gao S, Chen J, et al., and Zhao H, “Temporal dynamics and physical priori multimodal network for rehabilitation physical training evaluation”, IEEE Journal of Biomedical and Health Informatics, 2024.

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Publications List

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.