Faculty
Dr. Xinke Shen is a Research Assistant Professor in the Department of Biomedical Engineering at the Southern University of Science and Technology (SUSTech). He received his Bachelor's degree from Beihang University in 2017 and his Ph.D. from Tsinghua University in 2023. He joined SUSTech as a postdoctoral researcher in 2023 and was honored as a "President’s Distinguished Postdoctoral Fellow." Dr. Shen’s research focuses on brain-computer interfaces (BCI) and affective computing, with the goal of developing emotion-aware BCI systems for mental health monitoring and modulation. He has published 9 first-author or co-first-author papers in internationally renowned journals and conferences, including NeuroImage, IEEE Transactions on Affective Computing, and Advanced Materials, with 2 ESI Highly Cited Papers and total citations exceeding 1,000. He currently leads one Shenzhen Outstanding Scientific Innovation Talent Development (Doctoral Startup) Project and plays a key role in a Shenzhen Major Science and Technology Project. Dr. Shen has filed 4 national invention patents, with 2 already granted. Additionally, he has won awards such as the Second Prize and First Prize in the BCI Brain-Controlled Robot Competition at the World Robot Conference.
Education:
1) 2017.09 - 2023.01: Ph.D., Biomedical Engineering, School of Medicine, Tsinghua University. Advisor: Prof. Sen Song.
2) 2013.09 - 2017.06: B.S., Biomedical Engineering, School of Biological and Medical Engineering, Beihang University.
Research Experience:
2023.03 - 2025.03: Postdoctoral Fellow, Department of Biomedical Engineering, College of Engineering, Southern University of Science and Technology. Advisor: Prof. Quanying Liu.
Research Projects:
1) 2024 - 2026: Research on Contrastive Learning-based Emotion Brain-Computer Interface Algorithms (Shenzhen Outstanding Scientific Innovation Talent Development Doctoral Startup Project, ￥300,000, Leader).
2) 2024 - 2025: Development of Key Technologies for a Closed-Loop Neuromodulation System Combining High-Density Transcranial Electrical Stimulation and EEG Recordings (Shenzhen Major Science and Technology Project, ￥2,000,000, Core Contributor).
Publications:
1) Shen, X.#, Tao, L.#, Chen, X., Song, S., Liu, Q.*, Zhang, D.* (2024). Contrastive Learning of Shared Spatiotemporal EEG Representations Across Individuals for Naturalistic Neuroscience. Neuroimage, 301: 120890.
2) Shen, X.#, Liu, X.#, Hu, X., Zhang, D.*, & Song, S.* (2022). Contrastive learning of subject-invariant EEG representations for cross-subject emotion recognition. IEEE Transactions on Affective Computing, 14 (3): 2496-2511. (ESI Highly Cited Paper)
3) Shen, X., Liu, T.*, Tao, D., Fan, Y., Zhang, J., Li, S., Jiang, J., Zhu, W., Wang, Y., Wang, Y., Brodaty, H., Sachdev., P. & Wen, W. (2018). Variation in longitudinal trajectories of cortical sulci in normal elderly. Neuroimage, 166, 1-9.
4) Shen, X., Hu, X., Liu, S., Song, S., & Zhang, D.* (2020). Exploring EEG microstates for affective computing: decoding valence and arousal experiences during video watching. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 841-846). IEEE.
5) Xue, S.#, Shen, X.#, Zhang, D., Sang, Z., Long, Q., Song, S.*, Wu, J.* (2025). Unveiling frequency-specific microstate correlates of anxiety and depression symptoms[J]. Brain Topography, 38(1): 12.
6) Tang, J.#, Yuan, F.#, Shen, X.#, Wang, Z., Rao, M., He, Y., Sun, Y., Li, X., Zhang, W., Li, Y., Gao, B., Qian, H., Bi, G., Song, S., Yang, J. J.* & Wu, H.* (2019). Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges. Advanced Materials, 31(49), 1902761. (ESI Highly Cited Paper)
7) Yang, P.#, Shen, X.#, Li, Z., Luo, Z., Lou, K., & Liu, Q. (2023). Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data. IJCAI-AI4TS workshop.
8) Liu, J.#, Shen, X.#, Song, S., & Zhang, D.* (2021). Domain Adaptation for Cross-Subject Emotion Recognition by Subject Clustering. In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 904-908). IEEE.
9) Shen, X., Li, Y., Liu, J., Song, S., Zhang, D.* (2021). Emotional state decoding using EEG-based microstates of functional connectivity[J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(1): 49-58.
10) Liu, J., Hu, X., Shen, X., Lv, Z., Song, S., & Zhang, D.* (2023). The EEG microstate representation of discrete emotions. International Journal of Psychophysiology, 186, 33-41.
11) Liu, J., Hu, X., Shen, X., Song, S., & Zhang, D. (2023). Electrophysiological Representations of Multivariate Human Emotion Experience. Cognition and Emotion, 38(3), 378-388.
12) Chen, J., Wang, X., Huang, C., Hu, X., Shen, X., Zhang, D. (2023). A large finer-grained affective computing EEG dataset. Scientific Data, 10(1): 740.
13) Ke, L., Zhang, Y., Fu, Y., Shen, X., Zhang, Y., Ma, X., & Di, Q. (2022). Short-term PM2. 5 exposure and cognitive function: association and neurophysiological mechanisms. Environment International, 107593.
Patents:
1) Wang, Z., Shen, X., Xu, J., Xin, S., Fan, H., Wang, H., Zhou, Y., Ren, L., Yangdan, C., Ma, J., Wang, Z., A Neural Network-Based Segmentation Method and System for Hepatic Echinococcosis Lesions. CN201811548266.4[P]. 2019-04-26. (Authorized)
2) Wu, J., Song, S., Xue, S., Shen, X., Sang, Z. Microstate Source Localization Method, Device, Electronic Equipment, and Storage Medium. CN202310752490.X[P]. 2023-10-03. (Authorized)
3) Li, C., Shen, X., Huang, C., Zhang, D. Emotion Recognition Method, Device, Chip, Electronic Equipment, and Medium. CN202211175016.7[P]. 2022-09-26. (Under Review)
4) Zhang, D., Guo, B., Wang, F., Shen, X., Chen, J., Hu, X. EEG Signal Classification Method, Device, Equipment, and Storage Medium. CN202211182344.X[P]. 2022-12-23. (Under Review)