N21, University of Macau
(853) 8822-9370
(853) 8822 2456
ici.cogbrainsci@um.edu.mo
- Ph.D., Health Informatics (Computer Science), The Hong Kong Polytechnic University (2023)
- Master of Science, Health Informatics (Computer Science), The University of Sydney (2020)
- Bachelor of Science, Applied Psychology, South China Normal University (2017)
- 2026.4 -, Assistant Professor in AI and Cognitive Neuroscience, Centre for Cognitive and Brain Sciences, University of Macau
- 2023 – 2025, Postdoctoral Fellow, Dystonia and Speech Motor Control Laboratory, Harvard Medical School
- 2022 – 2023, Visiting Ph.D. Student, Department of Electrical and Computer Engineering, The University of Alberta
- Distinguished Ph.D. Thesis Award, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University (2023)
- Master’s Thesis Award, School of Computer Science, The University of Sydney (2020)
- AAAI conference traveling award (2022)
- My research interests focus on electrophysiological signal processing and brain–computer interface (BCI) systems. In particular, I study both non-invasive and invasive neural signal analysis for BCI applications. On the non-invasive side, my work involves the analysis of scalp electroencephalography (EEG) signals to decode brain activity and develop practical BCI systems. On the invasive side, I investigate cortical and subcortical local field potentials to enable more precise neural decoding and support high-performance BCI applications. Overall, my research aims to advance signal processing and analytical methods for neural data, contributing to more reliable and efficient BCI systems.
- Ad-hoc Reviewer for IEEE Transactions on Biomedical Engineering; IEEE Transactions on Neural Systems and Rehabilitation Engineering; IEEE Journal of Biomedical and Health Informatics; ECCV; AAAI; etc.
- X. Huang, S. Liang, Y. Zhang, N. Zhou, W. Pedrycz, and K.-S. Choi, “Relation learning using temporal episodes for motor imagery brain-computer interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023. doi: 10.1109/TNSRE.2022.3228216.
- X. Huang, K.-S. Choi, S. Liang, et al., “Frequency domain channel-wise attack to CNN classifiers in motor imagery brain-computer interfaces,” IEEE Transactions on Biomedical Engineering, 2023. doi: 10.1109/TBME.2023.3344295.
- X. Huang, K.-S. Choi, N. Zhou, Y. Zhang, B. Chen, and W. Pedrycz, “Shallow inception domain adaptation network for EEG-based motor imagery classification,” IEEE Transactions on Cognitive and Developmental Systems, 2023. doi: 10.1109/TCDS.2023.3279262.
- X. Huang, N. Zhou, and K.-S. Choi, “A discriminative and robust feature learning approach for EEG-based motor imagery decoding (student abstract),” Proceedings of the AAAI Conference on Artificial Intelligence, 2022. doi: 10.1609/aaai.v36i11.21622.
- X. Huang, N. Zhou, J. Huang, H. Zhang, W. Pedrycz, and K.-S. Choi, “Center transfer for supervised domain adaptation,” Applied Intelligence, 2023. doi: 10.1007/s10489-022-04414-2.
- X. Huang, N. Zhou, and K.-S. Choi, “A generalizable and discriminative learning method for deep EEG-based motor imagery classification,” Frontiers in Neuroscience, 2021. doi: 10.3389/fnins.2021.760979.
- X. Huang, S. Liang, Z. Li, C. Y. Y. Lai, and K.-S. Choi, “EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review,” Plos One, 2022. doi: 10.1371/journal.pone.0269001.