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 and Wednesdays 11.00am-noon or contact me to make an appointment in advance

Profile

Biography

He is a Lecturer in the School of Computer Science and Electronic Engineering at the University of Essex, UK. Before joining the Essex Uni, 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, pattern recognition, and neuroimaging data analytics by means of machine learning techniques (e.g., deep learning). Specifically, his work is to understand mental states, such as mental fatigue, reveal neural mechanisms pertaining to mental states, brain diseases, and aging, and classify brain patterns based on neuroimaging data for assistant diagnosis. He is a Senior Member of the IEEE.

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

To develop novel algorithms for pattern recognition and classification.

Key words: Deep Learning
Open to supervise

Computational Neuroscience

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

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

Teaching and supervision

Current teaching responsibilities

  • Group Project and Industrial Practice (CE201)

Publications

Journal articles (14)

Li, J., Thakor, N. and Bezerianos, A., Brain functional connectivity in unconstrained walking with and without an exoskeleton. IEEE Transactions on Neural Systems and Rehabilitation Engineering

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

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., 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., 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

Bose, R., Wang, H., Dragomir, A., Thakor, N., Bezerianos, A. and Li, J., (2019). Regression Based Continuous Driving Fatigue Estimation: Towards Practical Implementation. IEEE Transactions on Cognitive and Developmental Systems, 1-1

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., 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-

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

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)

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

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

Conferences (4)

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

Contact

junhua.li@essex.ac.uk

Location:

5A.535, Colchester Campus

Academic support hours:

Tuesdays and Wednesdays 11.00am-noon or contact me to make an appointment in advance