Yingchun Zhang

Yingchun Zhang, PhD

Assistant Affiliate Member, Research Institute
Houston Methodist


Biography

Dr. Zhang earned his Ph.D. in Electrical Engineering from Zhejiang University, China in 2004. He held a faculty appointment at the University of Minnesota, Minneapolis before joining the faculty of the University of Houston in 2012 and becoming an assistant affiliate member of Houston Methodist Research Institute in 2014. Dr. Zhang is currently an Assistant Professor in the Department of Biomedical Engineering at the University of Houston. He directs a predictive modeling and functional imaging research program focusing on clinical diagnosis technology of urogenital dysfunction and other disorders in the human body through the fusion of functional bioelectrical activity/impedance imaging, electrical stimulation and recording, advanced computational modeling and electrophysiological/biomechanical analysis. Dr. Zhang is a recipient of NIH Pathway to Independence (K99/R00) award, an IEEE senior member and serves as a reviewer for a number of peer-review scientific journals.

Description of Research

Dr. Zhang’s Predictive Modeling and Functional Imaging Lab is interested in advancing clinical diagnosis of dysfunction in the human body through the fusion of functional bioelectrical activity/property imaging, neuromodulation, advanced computational modeling and electrophysiological/biomechanical analysis with applications in investigating the mechanisms of bioelectrical activities and neural pathways in biological systems. His lab is also interested in utilizing developed functional imaging technologies to enhance human-machine-interface systems such as brain-computer-interface systems, brain-controlled devices and myoelectrically-controlled prostheses. The research in his lab involves both technology developments and experimental studies in human subjects and animals.

Areas Of Expertise

Multimodal neural imaging Predictive modeling Incontinence Rehabilitation Prostate cancer early detection
Education & Training

MS, Harbin Institute of Technology
Postdoctoral Fellowship, University of Minnesota
PhD, Zhejiang University
Publications

Cross-subject EEG emotion recognition using multi-source domain manifold feature selection
She, Q, Shi, X, Fang, F, Ma, Y & Zhang, Y 2023, , Computers in Biology and Medicine, vol. 159, 106860, pp. 106860. https://doi.org/10.1016/j.compbiomed.2023.106860

Multi-domain feature analysis method of MI-EEG signal based on Sparse Regularity Tensor-Train decomposition
Gao, Y, Zhang, C, Fang, F, Cammon, J & Zhang, Y 2023, , Computers in Biology and Medicine, vol. 158, 106887. https://doi.org/10.1016/j.compbiomed.2023.106887

Classification of Working Memory Loads via Assessing Broken Detailed Balance of EEG-FNIRS Neurovascular Coupling Measures
Gao, Y, Liu, H, Fang, F & Zhang, Y 2023, , IEEE Transactions on Biomedical Engineering, vol. 70, no. 3, pp. 877-887. https://doi.org/10.1109/TBME.2022.3204718

Predicting Antidepressant Treatment Response Using Functional Brain Controllability Analysis
Fang, F, Godlewska, B, Selvaraj, S & Zhang, Y 2023, , Brain Connectivity, vol. 13, no. 2, pp. 107-116. https://doi.org/10.1089/brain.2022.0027

Motor Unit Number Estimation in Spastic Biceps Brachii Muscles of Chronic Stroke Survivors Before and After BoNT Injection
Liu, Y, Chen, YT, Zhang, C, Zhou, P, Li, S & Zhang, Y 2023, , IEEE Transactions on Biomedical Engineering, vol. 70, no. 3, pp. 1045-1052. https://doi.org/10.1109/TBME.2022.3208078

Improved Domain Adaptation Network Based on Wasserstein Distance for Motor Imagery EEG Classification
She, Q, Chen, T, Fang, F, Zhang, J, Gao, Y & Zhang, Y 2023, , IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 1137-1148. https://doi.org/10.1109/TNSRE.2023.3241846

Double Stage Transfer Learning for Brain-Computer Interfaces
Gao, Y, Li, M, Peng, Y, Fang, F & Zhang, Y 2023, , IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 1128-1136. https://doi.org/10.1109/TNSRE.2023.3241301

A Novel Muscle Innervation Zone Estimation Method Using Monopolar High Density Surface Electromyography
Huang, C, Chen, M, Zhang, Y, Li, S, Klein, CS & Zhou, P 2023, , IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 22-30. https://doi.org/10.1109/TNSRE.2022.3215612

Hybrid EEG-fNIRS Brain Computer Interface Based on Common Spatial Pattern by Using EEG-Informed General Linear Model
Gao, Y, Jia, B, Houston, M & Zhang, Y 2023, , IEEE Transactions on Instrumentation and Measurement, vol. 72, 4006110. https://doi.org/10.1109/TIM.2023.3276509

Self-Supervised EEG Emotion Recognition Models Based on CNN
Wang, X, Ma, Y, Cammon, J, Fang, F, Gao, Y & Zhang, Y 2023, , IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 1952-1962. https://doi.org/10.1109/TNSRE.2023.3263570

Test and re-test reliability of optimal stimulation targets and parameters for personalized neuromodulation
Fang, F, Cammon, J, Li, R & Zhang, Y 2023, , Frontiers in Neuroscience, vol. 17, 1153786. https://doi.org/10.3389/fnins.2023.1153786

Multisource Associate Domain Adaptation for Cross-Subject and Cross-Session EEG Emotion Recognition
She, Q, Zhang, C, Fang, F, Ma, Y & Zhang, Y 2023, , IEEE Transactions on Instrumentation and Measurement, vol. 72, 2515512. https://doi.org/10.1109/TIM.2023.3277985

Dual-Encoder VAE-GAN With Spatiotemporal Features for Emotional EEG Data Augmentation
Tian, C, Ma, Y, Cammon, J, Fang, F, Zhang, Y & Meng, M 2023, , IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 2018-2027. https://doi.org/10.1109/TNSRE.2023.3266810

Joint Filter-Band-Combination and Multi-View CNN for Electroencephalogram Decoding
Fan, Z, Xi, X, Gao, Y, Wang, T, Fang, F, Houston, M, Zhang, Y, Li, L & Lu, Z 2023, , IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 2101-2110. https://doi.org/10.1109/TNSRE.2023.3269055

Personalizing repetitive transcranial magnetic stimulation for precision depression treatment based on functional brain network controllability and optimal control analysis
Fang, F, Godlewska, B, Cho, RY, Savitz, SI, Selvaraj, S & Zhang, Y 2022, , NeuroImage, vol. 260, 119465. https://doi.org/10.1016/j.neuroimage.2022.119465

Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review
Li, R, Yang, D, Fang, F, Hong, KS, Reiss, AL & Zhang, Y 2022, , Sensors, vol. 22, no. 15, 5865. https://doi.org/10.3390/s22155865

Effects of escitalopram therapy on functional brain controllability in major depressive disorder
Fang, F, Godlewska, B, Cho, RY, Savitz, SI, Selvaraj, S & Zhang, Y 2022, , Journal of Affective Disorders, vol. 310, pp. 68-74. https://doi.org/10.1016/j.jad.2022.04.123

Motor unit distribution and recruitment in spastic and non-spastic bilateral biceps brachii muscles of chronic stroke survivors
Liu, Y, Chen, YT, Zhang, C, Zhou, P, Li, S & Zhang, Y 2022, , Journal of neural engineering, vol. 19, no. 4, 046047. https://doi.org/10.1088/1741-2552/ac86f4

A new feature selection approach for driving fatigue EEG detection with a modified machine learning algorithm
Zheng, Y, Ma, Y, Cammon, J, Zhang, S, Zhang, J & Zhang, Y 2022, , Computers in Biology and Medicine, vol. 147, 105718, pp. 105718. https://doi.org/10.1016/j.compbiomed.2022.105718

EEG emotion recognition based on enhanced SPD matrix and manifold dimensionality reduction
Gao, Y, Sun, X, Meng, M & Zhang, Y 2022, , Computers in Biology and Medicine, vol. 146, 105606. https://doi.org/10.1016/j.compbiomed.2022.105606

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