Clearing 2021
People

Dr Junhua LI

Lecturer
School of Computer Science and Electronic Engineering (CSEE)
Dr Junhua LI
  • Email

  • Location

    5A.535, Colchester Campus

  • Academic support hours

    Tuesdays at 14.00 via Zoom (930 4730 8887)

Profile

Biography

He is a Lecturer in the School of Computer Science and Electronic Engineering at the University of Essex, UK. Before joining the University of Essex, he was a Senior Research Fellow at the National University of Singapore, Singapore. He obtained a PhD in Computer Science at the Shanghai Jiao Tong University, China. Given his background of computer science and computational neuroscience, he focuses on the researches of brain-computer interface, neurophysiological signal processing, machine learning, neuroimaging data analytics, as well as their practical applications. He is involved in a wide range of academic activities such as Associate Editor of the IEEE Access. He is a Senior Member of the IEEE. It is now open to recruiting postgraduate research students (also known as PhD students). If you are interested in any research topics below and have funding to support your study, please contact me for further discussions. (1) Developing machine learning algorithms (e.g., deep learning and tensor decomposition) for diverse applications (2) Brain-computer interface and health monitoring systems (3) Data analysis for understanding brain diseases and brain cognition Selected Publications: -Tian Wang, Anastasios Bezerianos, Andrzej Cichocki, Junhua Li*, Multi-Kernel Capsule Network for Schizophrenia Identification, IEEE Transactions on Cybernetics, 2020, DOI: 10.1109/TCYB.2020.3035282 -Junhua Li*, Thoughts on Neurophysiological Signal Analysis and Classification, Brain Science Advances, 6(3), 210-223, 2020 -Junhua Li*, Nitish Thakor, Anastasios Bezerianos, Brain Functional Connectivity in Unconstrained Walking with and without An Exoskeleton, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(3), 730-739, 2020 -Jonathan Harvy, Nitish Thakor, Anastasios Bezerianos, Junhua Li*, Between-Frequency Topographical and Dynamic High-Order Functional Connectivity for Driving Drowsiness Assessment, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(3), 358-367, 2019 -Junhua Li*, Rafael Romero-Garcia, John Sucking, Lei Feng*, Habitual Tea Drinking Modulates Brain Efficiency: Evidence from Brain Connectivity Evaluation, Aging, 11(11), 3876-3890, 2019 -Sim Kuan Goh, Hussein A. Abbass, Kay Chen Tan, Abdullah Al-Mamun, Nitish Thakor, Anastasios Bezerianos, Junhua Li*, Spatio-spectral Representation Learning for Electroencephalographic Gait-pattern Classification, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(9), 1858-1867, 2018 -Yu Sun, Julian Lim, Zhongxiang Dai, KianFoong Wong, Fumihiko Taya, Yu Chen, Junhua Li, Nitish Thakor, Anastasios Bezerianos, The Effects of A Mid-task Break on the Brain Connectome in Healthy Participants: A Resting-state Functional MRI Study, NeuroImage, 152, 19-30, 2017 -Junhua Li*, Chao Li, Andrzej Cichocki, Canonical Polyadic Decomposition with Auxiliary Information for Brain-Computer Interface, IEEE Journal of Biomedical and Health Informatics, 21(1), 263-271, 2017

Appointments

University of Essex

  • Lecturer, School of Computer Science and Electronic Engineering, University of Essex (1/5/2019 - present)

Research and professional activities

Research interests

Machine Learning and Artificial Intelligence (Developing novel algorithms for classification and recognition; Detecting brain diseases based on neuroimaging data; Developing systems of monitoring human health)

To develop novel algorithms for pattern recognition and classification.

Key words: Deep Learning
Open to supervise

Computational Neuroscience (Understanding mechanisms of the brain pertaining to brain diseases, ageing, and mental states)

To analyse a wide range of data such as fMRI, TDI, EEG, EMG and PET and reveal neural mechanisms pertaining to cognition, emotion, and brain diseases.

Key words: Brain Connectivity
Open to supervise

Brain Health and Signal Processing (Providing insights into the brain health based on signals; Techniques for keeping the brain healthy and augmenting the brain capacity)

To investigate brain health-related issues based on signal processing and machine learning.

Open to supervise

Teaching and supervision

Current teaching responsibilities

  • Team Project Challenge (CE201)

  • Neural Networks and Deep Learning (CE889)

Publications

Journal articles (40)

Wang, H., Liu, X., Li, J., Xu, T., Bezerianos, A., Sun, Y. and Wan, F., (2021). Driving Fatigue Recognition with Functional Connectivity Based on Phase Synchronization. IEEE Transactions on Cognitive and Developmental Systems. 13 (3), 668-678

Pei, Z., Wang, H., Bezerianos, A. and Li, J., (2021). EEG-Based Multi-Class Workload Identification Using Feature Fusion and Selection. IEEE Transactions on Instrumentation and Measurement. 70, 1-8

Wang, H., Pei, Z., Xu, L., Xu, T., Bezerianos, A., Sun, Y. and Li, J., (2021). Performance Enhancement of P300 Detection by Multi-Scale-CNN. IEEE Transactions on Instrumentation and Measurement. 70, 1-12

Gong, S., Xing, K., Cichocki, A. and Li, J., (2021). Deep Learning in EEG: Advance of the Last Ten-Year Critical Period. IEEE Transactions on Cognitive and Developmental Systems, 1-1

Li, J., (2021). Editorial: Recent Developments of Deep Learning in Analyzing, Decoding, and Understanding Neuroimaging Signals. Frontiers in Neuroscience. 15, 652073-

Li, J., (2021). Thoughts on Neurophysiological Signal Analysis and Classification. Brain Science Advances. 6 (3), 210-223

Bose, R., Wang, H., Dragomir, A., Thakor, N., Bezerianos, A. and Li, J., (2020). Regression Based Continuous Driving Fatigue Estimation: Towards Practical Implementation. IEEE Transactions on Cognitive and Developmental Systems. 12 (2), 323-331

Wang, H., Tang, C., Xu, T., Li, T., Xu, L., Yue, H., Chen, P., Li, J. and Bezerianos, A., (2020). An Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding. IEEE Access. 8, 86850-86861

Wang, H., Xu, T., Tang, C., Yue, H., Chen, C., Xu, L., Pei, Z., Dong, J., Bezerianos, A. and Li, J., (2020). Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection. IEEE Access. 8, 155590-155601

Zhu, L., Su, C., Zhang, J., Cui, G., Cichocki, A., Zhou, C. and Li, J., (2020). EEG-based approach for recognizing human social emotion perception. Advanced Engineering Informatics. 46, 101191-101191

Wang, T., Bezerianos, A., Cichocki, A. and Li, J., (2020). Multi-Kernel Capsule Network for Schizophrenia Identification. IEEE Transactions on Cybernetics. Early Access, 1-10

Li, J., Thakor, N. and Bezerianos, A., (2020). Brain functional connectivity in unconstrained walking with and without an exoskeleton. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 28 (3), 730-739

Feng, L., Romero-Garcia, R., Suckling, J., Tan, J., Larbi, A., Cheah, I., Wong, G., Tsakok, M., Lanskey, B., Lim, D., Li, J., Yang, J., Goh, B., Teck, TGC., Ho, A., Wang, X., Yu, J-T., Zhang, C., Tan, C., Chua, M., Li, J., Totman, JJ., Wong, C., Loh, M., Foo, R., Tan, CH., Goh, LG., Mahendran, R., Kennedy, BK. and Kua, E-H., (2020). Effects of choral singing versus health education on cognitive decline and aging: a randomized controlled trial. Aging. 12 (24), 24798-24816

Zhu, L., Zhou, C., Qu, Z. and Li, J., (2019). Monitoring time‐varying residential load operation modes: an efficient signal disaggregation approach. IEEJ Transactions on Electrical and Electronic Engineering. 14 (1), 85-96

Li, J., Dimitrakopoulos, GN., Thangavel, P., Chen, G., Sun, Y., Guo, Z., Yu, H., Thakor, N. and Bezerianos, A., (2019). What Are Spectral and Spatial Distributions of EEG-EMG Correlations in Overground Walking? An Exploratory Study. IEEE Access. 7, 143935-143946

Bose, R., Goh, SK., Wong, KF., Thakor, N., Bezerianos, A. and Li, J., (2019). Classification of Brain Signal (EEG) Induced by Shape-Analogous Letter Perception. Advanced Engineering Informatics. 42, 100992-100992

Li, J., Romero-Garcia, R., Suckling, J. and Feng, L., (2019). Habitual tea drinking modulates brain efficiency: evidence from brain connectivity evaluation. Aging. 11 (11), 3876-3890

Harvy, J., Thakor, N., Bezerianos, A. and Li, J., (2019). Between-Frequency Topographical and Dynamic High-Order Functional Connectivity for Driving Drowsiness Assessment. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 27 (3), 358-367

Li, J., Sun, Y., Huang, Y., Bezerianos, A. and Yu, R., (2019). Machine learning technique reveals intrinsic characteristics of schizophrenia: an alternative method. Brain Imaging and Behavior. 13 (5), 1386-1396

Li, J., Thakor, N. and Bezerianos, A., (2018). Unilateral Exoskeleton Imposes Significantly Different Hemispherical Effect in Parietooccipital Region, but Not in Other Regions. Scientific Reports. 8 (1), 13470-

Wang, H., Dragomir, A., Abbasi, NI., Li, J., Thakor, NV. and Bezerianos, A., (2018). A novel real-time driving fatigue detection system based on wireless dry EEG. Cognitive Neurodynamics. 12 (4), 365-376

Yokota, T., Struzik, ZR., Jurica, P., Horiuchi, M., Hiroyama, S., Li, J., Takahara, Y., Ogawa, K., Nishitomi, K., Hasegawa, M. and Cichocki, A., (2018). Semi-Automated Biomarker Discovery from Pharmacodynamic Effects on EEG in ADHD Rodent Models. Scientific Reports. 8 (1), 5202-

Jurica, P., Struzik, ZR., Li, J., Horiuchi, M., Hiroyama, S., Takahara, Y., Nishitomi, K., Ogawa, K. and Cichocki, A., (2018). Combining behavior and EEG analysis for exploration of dynamic effects of ADHD treatment in animal models. Journal of Neuroscience Methods. 298, 24-32

Goh, SK., Abbass, HA., Tan, KC., Al-Mamun, A., Thakor, N., Bezerianos, A. and Li, J., (2018). Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 26 (9), 1858-1867

Sun, Y., Li, J., Suckling, J. and Feng, L., (2017). Asymmetry of Hemispheric Network Topology Reveals Dissociable Processes between Functional and Structural Brain Connectome in Community-Living Elders. Frontiers in Aging Neuroscience. 9 (NOV)

Dai, Z., de Souza, J., Lim, J., Ho, PM., Chen, Y., Li, J., Thakor, N., Bezerianos, A. and Sun, Y., (2017). EEG Cortical Connectivity Analysis of Working Memory Reveals Topological Reorganization in Theta and Alpha Bands. Frontiers in Human Neuroscience. 11

Li, J., Chen, Y., Taya, F., Lim, J., Wong, K., Sun, Y. and Bezerianos, A., (2017). A unified canonical correlation analysis-based framework for removing gradient artifact in concurrent EEG/fMRI recording and motion artifact in walking recording from EEG signal. Medical & Biological Engineering & Computing. 55 (9), 1669-1681

Sun, Y., Dai, Z., Li, J., Collinson, SL. and Sim, K., (2017). Modular-level alterations of structure-function coupling in schizophrenia connectome. Human Brain Mapping. 38 (4), 2008-2025

Ren, S., Li, J., Taya, F., deSouza, J., Thakor, NV. and Bezerianos, A., (2017). Dynamic Functional Segregation and Integration in Human Brain Network During Complex Tasks. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 25 (6), 547-556

Sun, Y., Lim, J., Dai, Z., Wong, K., Taya, F., Chen, Y., Li, J., Thakor, N. and Bezerianos, A., (2017). The effects of a mid-task break on the brain connectome in healthy participants: A resting-state functional MRI study. NeuroImage. 152, 19-30

Li, J., Li, C. and Cichocki, A., (2017). Canonical Polyadic Decomposition With Auxiliary Information for Brain–Computer Interface. IEEE Journal of Biomedical and Health Informatics. 21 (1), 263-271

Li, J., Lim, J., Chen, Y., Wong, K., Thakor, N., Bezerianos, A. and Sun, Y., (2016). Mid-Task Break Improves Global Integration of Functional Connectivity in Lower Alpha Band. Frontiers in Human Neuroscience. 10

Li, J., Wang, Y., Zhang, L., Cichocki, A. and Jung, T-P., (2016). Decoding EEG in Cognitive Tasks With Time-Frequency and Connectivity Masks. IEEE Transactions on Cognitive and Developmental Systems. 8 (4), 298-308

Bodala, IP., Li, J., Thakor, NV. and Al-Nashash, H., (2016). EEG and Eye Tracking Demonstrate Vigilance Enhancement with Challenge Integration. Frontiers in Human Neuroscience. 10

Dai, Z., Chen, Y., Li, J., Fam, J., Bezerianos, A. and Sun, Y., (2016). Temporal efficiency evaluation and small-worldness characterization in temporal networks. Scientific Reports. 6 (1)

Li, J., Struzik, Z., Zhang, L. and Cichocki, A., (2015). Feature learning from incomplete EEG with denoising autoencoder. Neurocomputing. 165, 23-31

Liu, Y., Li, M., Zhang, H., Wang, H., Li, J., Jia, J., Wu, Y. and Zhang, L., (2014). A tensor-based scheme for stroke patients’ motor imagery EEG analysis in BCI-FES rehabilitation training. Journal of Neuroscience Methods. 222, 238-249

LI, J., LIANG, J., ZHAO, Q., LI, JIE., HONG, KAN. and ZHANG, L., (2013). DESIGN OF ASSISTIVE WHEELCHAIR SYSTEM DIRECTLY STEERED BY HUMAN THOUGHTS. International Journal of Neural Systems. 23 (03), 1350013-1350013

Li, J. and Zhang, L., (2012). Active training paradigm for motor imagery BCI. Experimental Brain Research. 219 (2), 245-254

Li, J. and Zhang, L., (2010). Bilateral adaptation and neurofeedback for brain computer interface system. Journal of Neuroscience Methods. 193 (2), 373-379

Conferences (6)

Zian, P., Tao, X., Anastasios, B., Li, J., Yu, S. and Hongtao, W., (2020). The Effect of Longitudinal Training on Working Memory Capacities: An Exploratory EEG Study

Harvy, J., Ewen, JB., Thakor, N., Bezerianos, A. and Li, J., (2019). Cortical Functional Connectivity during Praxis in Autism Spectrum Disorder

Sigalas, E., Li, J., Bezerianos, A. and Antonopoulos, C., (2018). Emergence of chimera-like states in prefrontal-cortex macaque intracranial recordings

Harvy, J., Sigalas, E., Thakor, N., Bezerianos, A. and Li, J., (2018). Performance Improvement of Driving Fatigue Identification Based on Power Spectra and Connectivity Using Feature Level and Decision Level Fusions

He, J., Zhou, G., Wang, H., Sigalas, E., Thakor, N., Bezerianos, A. and Li, J., (2018). Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG

Dimitrakopoulos, GN., Kakkos, I., Vrahatis, AG., Sgarbas, K., Li, J., Sun, Y. and Bezerianos, A., (2017). Driving Mental Fatigue Classification Based on Brain Functional Connectivity

Contact

junhua.li@essex.ac.uk

Location:

5A.535, Colchester Campus

Academic support hours:

Tuesdays at 14.00 via Zoom (930 4730 8887)