Medical Information Egineering

Zhang, Zhiguo (Distinguished Professor)
School of Biomedical Engineering
Department of Medical Information Engineering
Professor
Intro:

BIOGRAPHICAL SKETCH

NAME: Zhang, Zhiguo

POSITION TITLE: Professor, School of Biomedical Engineering, Health Science Center, Shenzhen University

EDUCATION/TRAINING

INSTITUTION AND LOCATION

DEGREE

 

Completion Date

MM/YYYY

 

FIELD OF STUDY

 

Tianjin University, Tianjin, China

B. Eng

06/2000

Electrical Engineering

The University of Science and Technology of China, Hefei, China

M.Eng.

06/2003

Electrical Engineering

The University of Hong Kong, Hong Kong, China

Ph.D.

05/2008

Electrical Engineering

The University of Hong Kong, Hong Kong, China

Postdoctoral Fellow

10/2011

Electrical Engineering

 

A.   Personal Statement

My research covers a variety of topics in computational neuroscience, biomedical signal processing, neural engineering, and digital signal processing. Specifically, my research focus is to develop and apply advanced data analytics and machine learning techniques to analyze brain signals and images, and subsequently investigate how the brain dynamically coordinates and integrates neural circuits to support behavior and cognition and how such brain dynamics could be altered in neurological and psychiatric diseases. I am also interested in utilizing brain imaging data in practical and clinical applications, such as early diagnosis of neurologic disorders, brain-computer interfaces, and enhanc ement of cognitive performance. Research in my lab has been supported by various funding agencies, including the National Natural Science Foundation in China (NSFC), Ministry of Education (MoE) in Singapore and the Research Grant Council (RGC) in Hong Kong. I have published > 50 papers in peer-reviewed professional journals and > 1000 conference papers and abstracts.

 

 

B.   Positions and Honors

Positions and Employment

11/2007 – 12/2008      Research Assistant, Duchess of Kent Children’s Hospital, Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China

01/2011 – 05/2011      Visiting Research Fellow, Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom

10/2011 – 12/2014      Research Assistant Professor, Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

01/2015 – 12/2015      Assistant Professor, School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore

12/2015 – 07/2016      Professor, School of Data and Computer Science, Sun Yat-sen University, Zhuhai/Guangzhou, China

01/2015 – present       Visiting Associate Professor, Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

07/2016 – present       Professor, School of Biomedical Engineering, Health Science Center, Shenzhen University

 

 

Organizing International Meetings

1.    Co-organizer, the IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 12-15 Aug., 2012, Hong Kong.

2.    Co-organizer, the 19th International Conference on Digital Signal Processing (DSP), 20-24 Aug., 2014, Hong Kong.

3.    Co-organizer, the IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, 12-14 Jun., 2015, Shenzhen.

4.    Organizer, the 2nd Symposium of Young Pain Neuroscientists, 1-2 Apr., 2017, Shenzhen.

 

Other Experience and Honors

1.    Guest Editor, Engineering (by Chinese Academy of Engineering): 2018

2.    Corresponding Expert , Engineering (by Chinese Academy of Engineering): 2017-present

3.    Editorial Board Member, Journal of Medical Imaging and Health Informatics: 2014-2016

4.    Guest Editor, Journal of Medical Imaging and Health Informatics: 2015

 

C.   Contributions to Science (*: corresponding/co-corresponding author)

A full list of my publications is enclosed at the end of this document.

 

1.    Estimation of dynamic brain networks from functional neuroimaging data

Exploration of connectivity between spatially distributed brain regions is a central research theme in neuroscience. At present there is a rapidly increasing interest in the temporal variations of brain connectivity, which enables the exploration of how brain regions dynamically exchange information to support cognition and behavior and how such a coordination could be affected by neuropsychiatric disorders. This research is aimed to develop connectivity analysis methods to estimate how the human brain is dynamically updated and orchestrated. Further, we aim to investigate the functional significance and clinical relevance of dynamic brain networks.

1)    Z. N. Fu, Y. H. Tu, X. Di, B. B. Biswal, V. D. Calhoun, and Z. G. Zhang*, “Associations between functional connectivity dynamics and bold dynamics are heterogeneous across brain networks,” Frontiers in Human Neuroscience, vol. 11, Article 293, Dec. 2017.

2)    A. Tan, L. Hu, R. Chen, Y. S. Hung, and Z. G.Zhang*, “N1 magnitude of auditory evoked potentials and spontaneous functional connectivity between bilateral Heschl’s gyrus are coupled at inter-individual level,” Brain Connectivity, vol. 6, no. 6, pp. 496-504, Jul. 2016.

3)    X. Di, Z. N. Fu, S. C. Chan, Y. S. Hung, B. B. Biswal, and Z. G. Zhang*, “Task-related functional connectivity dynamics in a block-designed visual experiment,” Frontiers in Human Neuroscience, vol. 9, Article 543, Sep. 2015.

4)    Z. N. Fu, S. C. Chan, X. Di, B. B. Biswal, and Z. G. Zhang*, “Adaptive covariance estimation of non-stationary processes and its application to infer dynamic connectivity from fMRI,” IEEE Transactions on Biomedical Circuits and Systems, vol. 8, no. 2, pp. 228-239, Apr. 2014.

 

 

2.    Identification of neural correlates of human pain perception

Pain is a subjective first-person unpleasant multidimensional experience. Self-reporting is the gold standard to determine the presence, absence, and intensity of pain in clinical practice. However, self-reporting of pain fails to be used in some vulnerable populations (e.g., patients with disorders of consciousness), which can lead to various serious clinical problems. Therefore, the availability of a physiology-based assessment of pain would be of great importance in basic and clinical applications. This research is aimed at identifying the cortical activity related to the generation of painful perception.

1)    L. L. Li, G. Huang, Q. Q. Lin, J. Liu, S. L. Zhang, and Z. G. Zhang*, “magnitude and temporal variability of inter-stimulus EEG modulate the linear relationship between laser-evoked potentials and fast-pain perception,” Frontiers in Neuroscience, in press.

2)    Y. H. Tu, Z. N. Fu, A. Tan, G. Huang, L. Hu, Y. S. Hung, and Z. G. Zhang*, “A novel and effective fMRI decoding approach based on sliced inverse regression and its application to pain prediction,” Neurocomputing, vol. 273, pp. 373-384, Jan. 2018.

3)    Y. H. Tu, A. Tan, Y. R. Bai, Y. S. Hung, and Z. G. Zhang*, “Decoding subjective intensity of nociceptive pain from pre-stimulus and post-stimulus brain activities,” Frontiers in Computational Neuroscience, vol. 10, article 32, Apr. 2016.

4)    Y. R. Bai, G. Huang, Y. H. Tu, A. Tan, Y. S. Hung, and Z. G. Zhang*, “Normalization of pain-evoked neural responses using spontaneous EEG improves the performance of EEG-based cross-individual pain prediction,” Frontiers in Computational Neuroscience, vol. 10, article 31, Apr. 2016.

5)    Y. H. Tu, Z. G. Zhang*, A. Tan, W. W. Peng, Y. S. Hung, M. Moayedi, G. D. Iannetti, and L. Hu*, “Alpha and gamma oscillation amplitudes synergistically predict the perception of forthcoming nociceptive stimuli,” Human Brain Mapping, vol. 37, no. 2, pp. 501-514, Feb. 2016.

5)    G. Huang, P. Xiao, Y. S. Hung, G. D. Iannetti, Z. G. Zhang*, and L. Hu*, “A novel approach to predict subjective pain perception from single-trial laser-evoked potentials,” NeuroImage, vol. 81, no. 1, pp. 283-293, Nov. 2013.

6)    Z. G. Zhang#, L. Hu#, Y. S. Hung, A. Mouraux, and G. D. Iannetti, “Gamma-band oscillations in the primary somatosensory cortex - a direct and obligatory correlate of subjective pain intensity,” Journal of Neuroscience, vol. 32, no. 22, pp. 7429-7438, May 2012. [# Equal Contribution]

 

 

3.    Signal processing and machine learning for brain decoding

Signal processing and machine learning techniques are increasingly used to identify brain-activation patterns corresponding to external stimuli or cognitive/behavioral responses and to further infer mental states from brain signals. In this study, we aim to develop and apply signal processing and machine-learning techniques to identify discriminative neural features from high-dimensional neuroimaging data for higher prediction accuracy and better model interpretation. For example, we are interested in cross-subject prediction (the prediction model is trained on a cohort of individuals and applied to another individual), which would be of great clinical value.

1)    J. P. Zhang*, Y. Cui, J. R. Zhang, J. Zhang, Q. Zhou, Q. Liu*, and Z. G. Zhang*, “Closely spaced MEG source localization and functional connectivity analysis using a new pre-whitening invariance of noise space algorithm,” Neural Plasticity, Article ID 4890497, Feb. 2016.

2)    Y. Wang#, Z. G. Zhang#, X. Li, H. Cui, X. Xie, K. D. Luk, and Y. Hu, “Usefulness of time-frequency patterns of somatosensory evoked potentials in identification of the location of spinal cord injury,” Journal of Clinical Neurophysiology, vol. 32., no. 4, pp. 341-345, Aug. 2015. [# Equal Contribution]

3)    J. F. Wu, A. M. S. Ang, K. M. Tsui, H. C. Wu, Y. S. Hung, Y. Hu, J. N. F. Mak, S. C. Chan*, and Z. G. Zhang*, “Efficient implementation and design of a new single-channel electrooculography-based human-machine interface system,” IEEE Transactions on Circuits and Systems II-Express Briefs, vol. 62., no. 2, pp. 179-183, Feb. 2015.

4)    Y. H. Tu, Y. S. Hung, G. Huang, L. Hu, and Z. G. Zhang*, “An automated and fast approach to detect single-trial visual evoked potentials with application to brain–computer interface,” Clinical Neurophysiology, vol. 125, no. 12, pp. 2372-2383, Dec. 2014.

5)    L. Hu, P. Xiao, Z. G. Zhang*, A. Mouraux, and G. D. Iannetti*, “Single-trial time-frequency analysis of electrocortical signals: Baseline correction and beyond,” NeuroImage, vol. 84, no. 1, pp. 876-887, Jan. 2014.

6)    L. Hu#, Z. G. Zhang#, and Y. Hu, “A time-varying source connectivity approach to reveal human somatosensory information processing,” NeuroImage, vol. 62, no. 1, pp. 217-228, Aug. 2012. [# Equal Contribution]

7)    Z. G. Zhang, Y. S. Hung, and S. C. Chan, “Local polynomial modelling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 3, pp. 557-566, Mar. 2011.

8)    Z. G. Zhang, K. D. K. Luk, and Y. Hu, “Identification of detailed time-frequency components in somatosensory evoked potentials,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 18, no. 3, pp. 245-254, Jun. 2010.

9)    Z. G. Zhang, J. L. Yang, S. C. Chan, K. D. K. Luk, and Y. Hu, “Time-frequency component analysis of somatosensory evoked potentials in rats,” BioMedical Engineering OnLine, vol. 8, no. 4, DOI: 10.1186/1475-925X-8-4, Feb. 2009.

 

 

4.    Non-stationary signal processing methods and biomedical applications

Accurate identification of physiological activity underlying important information about the states and conditions of the human body is central in biomedical engineering applications, such as brain-computer interface. However, meaningful but weak information is usually buried in a high amount of background noise and artifacts, and, therefore, cannot be easily characterized. To address this problem, we aim to develop and implement advanced signal processing methods to obtain robust and reliable detection of underlying states of body organs, such as the brain, the muscles, etc.

1)    G. Huang, Z. E. Xian, Z. G. Zhang*, S. C. Li, X. Y. Zhu, “Divide-and-conquer muscle synergies: A new feature space decomposition approach for simultaneous multifunction myoelectric control,” Biomedical Signal Processing and Control, in press.

2)    X. Chen, H. Y. Wen, Q. L. Li, T. F. Wang, S. P. Chen, Y. P. Zheng, and Z. G. Zhang*, “Identifying transient patterns of in vivo muscle behaviors during isometric contraction by local polynomial regression,” Biomedical Signal Processing and Control, vol. 24, pp. 93-102, Feb. 2016.

3)    Z. G. Zhang, S. C. Chan, and C. Wang, “A new regularized adaptive windowed Lomb-periodogram for time-frequency analysis of nonstationary signals with impulsive components,” IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 8, pp. 2283-2304, Aug. 2012.

4)    X. Chen, Y. P. Zheng, J. Y. Guo, Z. Y. Zhu, S. C. Chan, and Z. G. Zhang*, “Sonomyographic responses during voluntary isometric ramp contraction of the human rectus femoris muscle,” European Journal of Applied Physiology, vol. 112, no. 7, pp. 2603-2614, Jul. 2012.

5)    Z. G. Zhang and S. C. Chan, “On kernel selection of multivariate local polynomial modelling and its application to image smoothing and reconstruction,” Journal of Signal Processing Systems for Signal Image and Video Technology, vol. 64, no. 3, pp. 361-374, Sep. 2011.

6)    Z. G. Zhang, H. T. Liu, S. C. Chan, K. D. K. Luk, and Y. Hu, “Time-dependent power spectral density estimation of surface electromyography during isometric muscle contraction: Methods and comparisons,” Journal of Electromyography and Kinesiology, vol. 20, no. 1, pp. 89-101, Feb. 2010.

7)    Z. G. Zhang, V. W. Zhang, S. C. Chan, B. McPherson, and Y. Hu, “Time-frequency analysis of click-evoked otoacoustic emissions by means of a minimum variance spectral estimation-based method,” Hearing Research, vol. 243, no. 1-2, pp. 18-27, Sep. 2008.

8)    Z. G. Zhang, S. C. Chan, K. L. Ho, and K. C. Ho, “On bandwidth selection in local polynomial regression analysis and its application to multi-resolution analysis of non-uniform data,” Journal of Signal Processing Systems for Signal Image and Video Technology, vol. 52, no. 3, pp. 263-280, Sep. 2008.

9)    Z. G. Zhang, K. M. Tsui, S. C. Chan, W. Y. Lau, and M. Aboy, “A novel method for nonstationary power spectral density estimation of cardiovascular pressure signals based on a Kalman filter with variable number of measurements,” Medical & Biological Engineering & Computing, vol. 46, no. 8, pp. 789-797, Aug. 2008.

10) Z. G. Zhang, S. C. Chan, and K. M. Tsui, “A recursive frequency estimator using linear prediction and a Kalman-filter-based iterative algorithm,” IEEE Transactions on Circuits and Systems II-Express Briefs, vol. 55, no. 6, pp. 576-580, Jun. 2008.

 

 

 

D.   Research Support

Ongoing Research Support

Shenzhen Science and Technology Innovation Commission grant (JCYJ20170818093322718)                                                                                                             03/2018 to 02/2021

Zhiguo Zhang, PI                                                                ¥2,000,000

Evaluation and Rehabilitation of Cognitive Functions of Brain Injury Patients based on Dynamic Brain Nwtworks

 

Completed Research Support

 

Small Project Funding (The University of Hong Kong)                                  01/2010 to 06/2011

Zhiguo Zhang, PI                                                                HK$79,525

A study of robust multivariate local polynomial modelling and its application for estimating time-varying cortical connectivity

 

Seed Funding for Basic Research (The University of Hong Kong)        03/2012 to 09/2013

Zhiguo Zhang, PI                                                                HK$120,000

A study of single-trial event-related potential detection method for brain-computer interface

 

Seed Funding for Basic Research (The University of Hong Kong)        07/2013 to 12/2014

Zhiguo Zhang, PI                                                                HK$50,500

Quantitative prediction of subjective pain perception from laser-evoked EEG responses

 

Seed Funding for Applied Research (The University of Hong Kong)     07/2013 to 12/2014

Zhiguo Zhang, PI                                                                HK$100,000

Development of a massively parallel GPU platform for real-time and automated feature extraction of evoked potentials

 

Seed Funding for Basic Research (The University of Hong Kong)        06/2014 to 11/2015

Zhiguo Zhang, PI                                                                HK$47,900

Localizing EEG signatures of pain perception in the brain: A simultaneous EEG-fMRI study

 

General Research Fund of Research Grants Council (Hong Kong)             01/2014 to 12/2015

Zhiguo Zhang, PI                                                                HK$562,921

A study of robust multivariate local polynomial modelling and its application for estimating time-varying cortical connectivity

 

MINDEF-NTU Joint Applied R&D Cooperation Programme (Singapore)     09/2015 to 02/2017

Zhiguo Zhang, PI                                                                SG$80,000

A Wearable Intelligent Brain Stimulation System for Adaptive Vigilance Enhancement in

Prolonged Military Operations

 

Academic Research Fund Tier 1 (Singapore)                                                      11/2015 to 11/2017

Zhiguo Zhang, PI                                                                SG$96,000

Real-time Decoding of Stimulus-evoked Pain Intensity from Spontaneous and Elicited Electroencephalography

 

Academic Research Fund Tier 2 (Singapore)                                                      01/2015 to 12/2017

Zhiguo Zhang, PI                                                                SG$503,800

EEG Feedforward Control of Working Memory Tasks for Adaptive Cognitive Training

 

National Natural Science Foundation of China (61640002)                                  01/2017 to 12/2017

Zhiguo Zhang, PI                                                                ¥160,000

Adaptive Estimation and Decoding of Dynamic Function Connectivity: New Methods and Application

 

 

 

E.    Peer-reviewed publications (*: corresponding author)

1.    L. L. Li, G. Huang, Q. Q. Lin, J. Liu, S. L. Zhang, and Z. G. Zhang*, “magnitude and temporal variability of inter-stimulus EEG modulate the linear relationship between laser-evoked potentials and fast-pain perception,” Frontiers in Neuroscience, in press.

2.    G. Huang, Z. E. Xian, Z. G. Zhang*, S. C. Li, X. Y. Zhu, “Divide-and-conquer muscle synergies: A new feature space decomposition approach for simultaneous multifunction myoelectric control,” Biomedical Signal Processing and Control, in press.

3.    Z. N. Fu, Y. H. Tu, X. Di, Y. H. Du, G. D. Pearlson, J. A. Turner, B. B. Biswal, Z. G. Zhang, and V. D. Calhoun, “Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia,” NeuroImage, in press.

4.    W. W. Peng, X. L. Xia, M. Yi, G. Huang, Z. G. Zhang, G. D. Iannetti, and L. Hu, “Brain oscillations reflecting pain-related behavior in freely-moving rats,” Pain, in press.

5.    Y. H. Tu, Z. N. Fu, A. Tan, G. Huang, L. Hu, Y. S. Hung, and Z. G. Zhang*, “A novel and effective fMRI decoding approach based on sliced inverse regression and its application to pain prediction,” Neurocomputing, vol. 273, pp. 373-384, Jan. 2018.

6.    Z. N. Fu, Y. H. Tu, X. Di, B. B. Biswal, V. D. Calhoun, and Z. G. Zhang*, “Associations between functional connectivity dynamics and bold dynamics are heterogeneous across brain networks,” Frontiers in Human Neuroscience, vol. 11, Article 293, Dec. 2017.

7.    J. L. Gao, J. C. Fan, B. Wu, G. T. Halkias, M. Chau, P. C. W. Fung, C. Q. Chang, Z. G. Zhang, Y. S. Hung, and H. H. Sik, “Repetitive religious chanting modulates the late-stage brain response to fear- and stress-provoking pictures,” Frontiers in Psychology, DOI: 10.3389/fpsyg.2016.02055, Jan. 2017.

8.    A. Tan, L. Hu, R. Chen, Y. S. Hung, and Z. G.Zhang*, “N1 magnitude of auditory evoked potentials and spontaneous functional connectivity between bilateral Heschl’s gyrus are coupled at inter-individual level,” Brain Connectivity, vol. 6, no. 6, pp. 496-504, Jul. 2016.

9.    Y. H. Tu, A. Tan, Y. R. Bai, Y. S. Hung, and Z. G. Zhang*, “Decoding subjective intensity of nociceptive pain from pre-stimulus and post-stimulus brain activities,” Frontiers in Computational Neuroscience, vol. 10, article 32, Apr. 2016.

10.  Y. R. Bai, G. Huang, Y. H. Tu, A. Tan, Y. S. Hung, and Z. G. Zhang*, “Normalization of pain-evoked neural responses using spontaneous EEG improves the performance of EEG-based cross-individual pain prediction,” Frontiers in Computational Neuroscience, vol. 10, article 31, Apr. 2016.

11.  J. L. Gao, J. C. Fan, B. Wu, Z. G. Zhang, C. Q. Chang, Y. S. Hung, P. C. W. Fung, and H. H. Sik, “Entrainment of chaotic activities in brain and heart during MBSR meditation training,” Neuroscience Letters, vol. 616, no. 2, pp. 218-223, Mar. 2016.

12.  Y. H. Tu, Z. G. Zhang*, A. Tan, W. W. Peng, Y. S. Hung, M. Moayedi, G. D. Iannetti, and L. Hu*, “Alpha and gamma oscillation amplitudes synergistically predict the perception of forthcoming nociceptive stimuli,” Human Brain Mapping, vol. 37, no. 2, pp. 501-514, Feb. 2016.

13.  X. Chen, H. Y. Wen, Q. L. Li, T. F. Wang, S. P. Chen, Y. P. Zheng, and Z. G. Zhang*, “Identifying transient patterns of in vivo muscle behaviors during isometric contraction by local polynomial regression,” Biomedical Signal Processing and Control, vol. 24, pp. 93-102, Feb. 2016.

14.  J. P. Zhang*, Y. Cui, J. R. Zhang, J. Zhang, Q. Zhou, Q. Liu*, and Z. G. Zhang*, “Closely spaced MEG source localization and functional connectivity analysis using a new pre-whitening invariance of noise space algorithm,” Neural Plasticity, Article ID 4890497, Feb. 2016.

15.  L. Hu, Z. G. Zhang, H. T. Liu, K. D. K. Luk, and Y. Hu, “Single-trial detection for intraoperative somatosensory evoked potentials monitoring,” Cognitive Neurodynamics, vol. 9, no. 6, pp. 589-601, Dec. 2015.

16.  X. Di, Z. N. Fu, S. C. Chan, Y. S. Hung, B. B. Biswal, and Z. G. Zhang*, “Task-related functional connectivity dynamics in a block-designed visual experiment,” Frontiers in Human Neuroscience, vol. 9, Article 543, Sep. 2015.

17.  Y. Wang#, Z. G. Zhang#, X. Li, H. Cui, X. Xie, K. D. Luk, and Y. Hu, “Usefulness of time-frequency patterns of somatosensory evoked potentials in identification of the location of spinal cord injury,” Journal of Clinical Neurophysiology, vol. 32., no. 4, pp. 341-345, Aug. 2015. [# Equal Contribution]

18.  L. Hu, Z. G. Zhang, A. Mouraux, and G. D. Iannetti, “Multiple linear regression to estimate time-frequency electrophysiological responses in single trials,” NeuroImage, vol. 111, no. 1, pp. 442-453, May 2015.

19.  J. F. Wu, A. M. S. Ang, K. M. Tsui, H. C. Wu, Y. S. Hung, Y. Hu, J. N. F. Mak, S. C. Chan*, and Z. G. Zhang*, “Efficient implementation and design of a new single-channel electrooculography-based human-machine interface system,” IEEE Transactions on Circuits and Systems II-Express Briefs, vol. 62., no. 2, pp. 179-183, Feb. 2015.

20.  Y. H. Tu, Y. S. Hung, G. Huang, L. Hu, and Z. G. Zhang*, “An automated and fast approach to detect single-trial visual evoked potentials with application to brain–computer interface,” Clinical Neurophysiology, vol. 125, no. 12, pp. 2372-2383, Dec. 2014.

21.  Z. N. Fu, S. C. Chan, X. Di, B. B. Biswal, and Z. G. Zhang*, “Adaptive covariance estimation of non-stationary processes and its application to infer dynamic connectivity from fMRI,” IEEE Transactions on Biomedical Circuits and Systems, vol. 8, no. 2, pp. 228-239, Apr. 2014.

22.  W. W. Peng, L. Hu, Z. G. Zhang, and Y. Hu, “Changes of spontaneous oscillatory activity to tonic heat pain,” PLOS ONE, vol. 9, no. 3, e91052, Jan. 2014.

23.  L. Hu, P. Xiao, Z. G. Zhang*, A. Mouraux, and G. D. Iannetti*, “Single-trial time-frequency analysis of electrocortical signals: Baseline correction and beyond,” NeuroImage, vol. 84, no. 1, pp. 876-887, Jan. 2014.

24.  L. Hu, E. Valentini, Z. G. Zhang, M. Liang, and G. D. Iannetti, “The primary somatosensory cortex contributes to the latest part of the cortical response elicited by noxious somatosensory stimuli in humans,” NeuroImage, vol. 84, no. 1, pp. 383-393, Jan. 2014.

25. Z. G. Zhang and S. C. Chan, “Recursive parametric frequency/spectrum estimation for non-stationary signals with impulsive components using variable forgetting factor,” IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 12, pp. 3251-3264, Dec. 2013.

26.  G. Huang, P. Xiao, Y. S. Hung, G. D. Iannetti, Z. G. Zhang*, and L. Hu*, “A novel approach to predict subjective pain perception from single-trial laser-evoked potentials,” NeuroImage, vol. 81, no. 1, pp. 283-293, Nov. 2013.

27.  L. Zhang, W. W. Peng, Z. G. Zhang, and L. Hu, “Distinct features of auditory steady-state evoked potentials as compared to transient event-related potentials,” PLOS ONE, vol. 8, no. 7, e69164, Jul. 2013.

28.  G. Huang, Z. G. Zhang, D. G. Zhang, and X. Y. Zhu, “Spatio-spectral filters for low-density surface electromyographic signal classification,” Medical & Biological Engineering & Computing, vol. 51, no. 5, pp. 547-555, May 2013.

29.  S. C. Chan, Y. J. Chu, Z. G. Zhang, and K. M. Tsui, “A new variable regularized QR decomposition-based recursive least M-estimate algorithm: Performance analysis and acoustic applications,” IEEE Transactions on Audio Speech and Language Processing, vol. 21, no.5, pp. 907-922, May 2013.

30.  S. C. Chan, Y. J. Chu, and Z. G. Zhang, “A new variable regularized transform domain NLMS adaptive filtering algorithm: Acoustic applications and performance analysis,” IEEE Transactions on Audio Speech and Language Processing, vol. 21, no. 4, pp. 868-878, Apr. 2013.

31.  L. Hu, W. W. Peng, E. Valentini, Z. G. Zhang, and Y. Hu, “Functional features of nociceptive-induced suppression of alpha band electroencephalographic oscillations,” Journal of Pain, vol. 14, no. 1, pp. 89-99, Jan. 2013.

32.  L. Hu#, Z. G. Zhang#, and Y. Hu, “A time-varying source connectivity approach to reveal human somatosensory information processing,” NeuroImage, vol. 62, no. 1, pp. 217-228, Aug. 2012. [# Equal Contribution]

33.  Z. G. Zhang, S. C. Chan, and C. Wang, “A new regularized adaptive windowed Lomb-periodogram for time-frequency analysis of nonstationary signals with impulsive components,” IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 8, pp. 2283-2304, Aug. 2012.

34.  X. Chen, Y. P. Zheng, J. Y. Guo, Z. Y. Zhu, S. C. Chan, and Z. G. Zhang*, “Sonomyographic responses during voluntary isometric ramp contraction of the human rectus femoris muscle,” European Journal of Applied Physiology, vol. 112, no. 7, pp. 2603-2614, Jul. 2012.

35.  S. C. Chan, Z. Y. Zhu, K. T. Ng, C. Wang, S. Zhang, Z. G. Zhang, Z. Ye, and H. Y. Shum, “The design and construction of a movable image-based rendering system and its application to multiview conferencing,” Journal of Signal Processing Systemsfor Signal Image and Video Technology, vol. 67, no. 3, pp. 305-316, Jun. 2012.

36.  Z. G. Zhang#, L. Hu#, Y. S. Hung, A. Mouraux, and G. D. Iannetti, “Gamma-band oscillations in the primary somatosensory cortex - a direct and obligatory correlate of subjective pain intensity,” Journal of Neuroscience, vol. 32, no. 22, pp. 7429-7438, May 2012. [# Equal Contribution]

37.  W. W. Peng, L. Hu, Z. G. Zhang, and Y. Hu, “Causality in the association between P300 and alpha event-related desynchronization,” PLOS ONE, vol. 7, no. 4, e34163, Apr. 2012.

38.  B. Liao, Z. G. Zhang, and S. C. Chan, “DOA estimation and tracking of ULAs with mutual coupling,” IEEE Transactions on Aerospace and Electronic Systems, vol. 48, no. 1, pp. 891-905, Jan. 2012.

39.  S. M. Lai, Z. G. Zhang, Y. S. Hung, Z. D. Niu, and C. Q. Chang, “A chromatic transient visual evoked potential based encoding/decoding approach for brain-computer interface,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 1, no. 4, pp. 578-589, Dec. 2011.

40.  Z. G. Zhang and S. C. Chan, “On kernel selection of multivariate local polynomial modelling and its application to image smoothing and reconstruction,” Journal of Signal Processing Systems for Signal Image and Video Technology, vol. 64, no. 3, pp. 361-374, Sep. 2011.

41.  V. W. Zhang, Z. G. Zhang, B. McPherson, Y. Hu, and Y. S. Hung, “Detection improvement for neonatal click evoked otoacoustic emissions by time-frequency filtering,” Computers in Biology and Medicine, vol. 41, no. 8, pp. 675-686, Aug. 2011.

42.  L. Hu, Z. G. Zhang, Y. S. Hung, K. D. K. Luk, G. D. Iannetti, and Y. Hu, “Single-trial detection of somatosensory evoked potentials by probabilistic independent component analysis and wavelet filtering,” Clinical Neurophysiology, vol. 122, no. 7, pp. 1429-1439, Jul. 2011.

43.  I. F. Su, D. K. Y. Lau, Z. G. Zhang, N. Yan, and S. P. Law, “Deficits in processing characters in Chinese developmental dyslexia: Preliminary results from event-related potentials and time frequency analyses,” International Journal of Linguistics, vol. 3, no. 1, E11, Jun. 2011.

44.  Z. G. Zhang, Y. S. Hung, and S. C. Chan, “Local polynomial modelling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 3, pp. 557-566, Mar. 2011.

45.  S. C. Chan and Z. G. Zhang, “Local polynomial modeling and variable bandwidth selection for time-varying linear systems,” IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 3, pp. 1102-1117, Mar. 2011.

46.  S. C. Chan, Z. G. Zhang, and Y. J. Chu, “A new transform-domain regularized recursive least M-estimate algorithm for robust linear estimation,” IEEE Transactions on Circuits and Systems II-Express Briefs, vol. 58, no. 2, pp. 120-124, Feb. 2011.

47.  B. Liao, Z. G. Zhang, and S. C. Chan, “A new robust Kalman filter-based subspace tracking algorithm in impulsive noise environment,” IEEE Transactions on Circuits and Systems II-Express Briefs, vol. 57, no. 9, pp. 740-744, Sep. 2010.

48.  Z. G. Zhang, K. D. K. Luk, and Y. Hu, “Identification of detailed time-frequency components in somatosensory evoked potentials,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 18, no. 3, pp. 245-254, Jun. 2010.

49.  F. F. Zhu, J. P. Maxwell, Y. Hu, Z. G. Zhang, W. K. Lam, J. M. Poolton, and R. S. W. Masters, “EEG activity during the verbal-cognitive stage of motor skill acquisition,” Biological Psychology, vol. 84, no. 2, pp. 221-227, May 2010.

50.  Z. G. Zhang, H. T. Liu, S. C. Chan, K. D. K. Luk, and Y. Hu, “Time-dependent power spectral density estimation of surface electromyography during isometric muscle contraction: Methods and comparisons,” Journal of Electromyography and Kinesiology, vol. 20, no. 1, pp. 89-101, Feb. 2010.

51.  Z. G. Zhang, J. L. Yang, S. C. Chan, K. D. K. Luk, and Y. Hu, “Time-frequency component analysis of somatosensory evoked potentials in rats,” BioMedical Engineering OnLine, vol. 8, no. 4, DOI: 10.1186/1475-925X-8-4, Feb. 2009.

52.  H. H. Chen, S. C. Chan, Z. G. Zhang, and K. L. Ho, “Adaptive beamforming and recursive DOA estimation using frequency invariant uniform concentric spherical arrays,” IEEE Transactions on Circuits and Systems I-Regular Papers, vol. 55, no. 10, pp. 3077-3089, Nov. 2008.

53.  Z. G. Zhang, V. W. Zhang, S. C. Chan, B. McPherson, and Y. Hu, “Time-frequency analysis of click-evoked otoacoustic emissions by means of a minimum variance spectral estimation-based method,” Hearing Research, vol. 243, no. 1-2, pp. 18-27, Sep. 2008.

54.  Z. G. Zhang, S. C. Chan, K. L. Ho, and K. C. Ho, “On bandwidth selection in local polynomial regression analysis and its application to multi-resolution analysis of non-uniform data,” Journal of Signal Processing Systems for Signal Image and Video Technology, vol. 52, no. 3, pp. 263-280, Sep. 2008.

55.  Z. G. Zhang, K. M. Tsui, S. C. Chan, W. Y. Lau, and M. Aboy, “A novel method for nonstationary power spectral density estimation of cardiovascular pressure signals based on a Kalman filter with variable number of measurements,” Medical & Biological Engineering & Computing, vol. 46, no. 8, pp. 789-797, Aug. 2008.

56.  H. Cheng, S. C. Chan, and Z. G. Zhang, “Robust channel estimation and multiuser detection for MC-CDMA systems under narrowband interference,” Journal of Signal Processing Systems for Signal Image and Video Technology, vol. 52, no. 2, pp. 165-180, Aug. 2008.

57.  Z. G. Zhang, S. C. Chan, and K. M. Tsui, “A recursive frequency estimator using linear prediction and a Kalman-filter-based iterative algorithm,” IEEE Transactions on Circuits and Systems II-Express Briefs, vol. 55, no. 6, pp. 576-580, Jun. 2008.

58.  V. W. Zhang, B. McPherson, and Z. G. Zhang, “Tone burst-evoked otoacoustic emissions in neonates: normative data,” BMC Ear, Nose and Throat Disorders, vol. 8, no. 3, DOI: 10.1186/1472-6815-8-3, Apr. 2018