Mr. Jing Zhang | Biomedical Signal Processing | Best Researcher Award
Mr. Jing Zhang | lecturer | Taiyuan University of Science and Technology | China
Jing Zhang is a dedicated researcher and lecturer at the School of Electronic Information Engineering, Taiyuan University of Science and Technology, China. His research primarily focuses on signal processing, emotion recognition, and video coding and transmission, with a strong interdisciplinary approach bridging neuroscience, artificial intelligence, and communication systems. His innovative work in multimodal neural signal analysis leverages EEG and fNIRS data to explore causal brain connectivity and emotional decoding. By integrating Granger causality with deep learning architectures such as convolutional and graph convolutional networks, as well as attention mechanisms, his research contributes significantly to affective computing and brain–computer interface (BCI) applications. Dr. Zhang has published several high-impact papers in reputed international journals indexed in SCI and Scopus, with over 75 citations and an h-index of 6 on Google Scholar, reflecting the growing influence and recognition of his work in the scientific community. His research outcomes demonstrate both theoretical and practical implications for advancing emotion-aware technologies, neuroadaptive systems, and hybrid video transmission models. His scholarly contributions include publications in prestigious journals such as IEEE Transactions on Circuits and Systems for Video Technology and Frontiers in Neuroscience.
Featured Publications
Zhang, J., Zhang, X., Chen, G., Huang, L., & Sun, Y. (2022). EEG emotion recognition based on cross-frequency Granger causality feature extraction and fusion in the left and right hemispheres. Frontiers in Neuroscience, 16, 974673.
Zhang, J., Wang, A., Liang, J., Wang, H., Li, S., & Zhang, X. (2018). Distortion estimation-based adaptive power allocation for hybrid digital–analog video transmission. IEEE Transactions on Circuits and Systems for Video Technology, 29(6), 1806–1818.
Zhang, J., Zhang, X., Chen, G., & Zhao, Q. (2022). Granger-causality-based multi-frequency band EEG graph feature extraction and fusion for emotion recognition. Brain Sciences, 12(12), 1649.
Chen, G., Zhang, X., Zhang, J., Li, F., & Duan, S. (2022). A novel brain-computer interface based on audio-assisted visual evoked EEG and spatial-temporal attention CNN. Frontiers in Neurorobotics, 16, 995552.
Li, P., Yang, F., Zhang, J., Guan, Y., Wang, A., & Liang, J. (2020). Synthesis-distortion-aware hybrid digital analog transmission for 3D videos. IEEE Access, 8, 85128–85139.