Li Yang has been with the School of Artificial Intelligence, Xidian University, where he is an associate professor with tenure. He received the Ph.D. degree in signal processing from the School of Information Science and Engineering at Southeast University. From August 2018 to August 2019, he was a jointly-trained doctoral student funded by the China Scholarship Council to study at the University of Wollongong in Australia. His main research areas are affective computing, brain-computer hybrid intelligence, and pattern recognition. He has published over 20 papers in leading international journals and conferences (such as IEEE TAFFC, IEEE TBME, IEEE TCDS, IJCAI, etc.), with 5 selected as ESI Highly Cited Papers and over 2,400 citations on Google Scholar. He is selected for the 2024 Stanford University-Elsevier "World's Top 2% of Scientists Annual Science Impact" list. He has served as an associated editor for IEEE transactions on Affective Computing, and reviewers for major international journals like TAFFC, TNNLS, TCDS, PR, NC, KNOSYS, ESWA and AIRE. He has led projects funded by the National Natural Science Foundation of China, the Natural Science Foundation of Shaanxi Province, the Special Funding and General Projects of China Postdoctoral Science Foundation,, and other national departmental projects. He has guided students to win five national and provincial awards, including a provincial silver award in the "Internet+" competition and a third prize in the national finals of the Graduate Electronic Design Contest, and has received three Outstanding Instructor awards.

Research Interests:

EEG-based emotion recognition, brain-computer interface (BCI), brain-computer hybrid intelligence, and pattern recognition

Paper Publication:

[1]    Boxun Fu, Fu Li, Youshuo Ji, Yang Li, Xuemei Xie, Xiaoli Li, "Improved Motor Imagery EEG Interdevice Decoding by Reweighting Multisource Domain Samples," IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-12, Art no. 2516812, 2024.

[2]    Youshuo Ji, Fu Li, Boxun Fu, Yijin Zhou, Hao Wu, Yang Li, Xiaoli Li, and Guangming Shi, "A novel hybrid decoding neural network for EEG signal representation[J]", Pattern Recognition, vol. 155, pp. 110726, 2024.

[3]    Yang Li, Ji Chen, Fu Li, Boxun Fu, Hao Wu, Youshuo Ji, Yijin Zhou, Yi Niu, Guangming Shi, and Wenming Zheng, “GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition[J],” IEEE Transactions on Affective Computing, vol. 14, no. 3, pp. 2512-2525, 2023. 【ESI Highly Cited Paper】

[4]    Tengfei Song, Suyuan Liu, Wenming Zheng, Yuan Zong, Zhen Cui, Yang Li, Xiaoyan Zhou, “Variational Instance-Adaptive Graph for EEG Emotion Recognition[J]”, IEEE Transactions on Affective Computing, vol. 14, no. 1, pp. 343-356, 2023.

[5]    Boxun Fu, Fu Li, Youshuo Ji, Yang Li, Xuemei Xie and Guangming Shi, “SCDAN: Learning Common Feature Representation of Brain Activation for Inter-Subject Motor Imagery EEG Decoding[J]”, IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-15, Art no. 2518315, 2023.

[6]    Yang Li, Wenming Zheng, Lei Wang, Yuan Zong, and Zhen Cui, “From Regional to Global Brain: A Novel Hierarchical Spatial-Temporal Neural Network Model for EEG Emotion Recognition[J]”, IEEE Transactions on Affective Computing, vol. 13, no. 2, pp. 568-578, 2022. 【ESI Highly Cited Paper】

[7]    Fu Li, Chong Wang, Yang Li*, Hao Wu, Boxun Fu, Youshuo Ji, Yi Niu, and Guangming Shi, “Phase Preservation Neural Network for Electroencephalography Classification in Rapid Serial Visual Presentation Task[J]”, IEEE Transactions on Biomedical Engineering, vol. 69, no. 6, pp. 1931-1942, 2022.

[8]    Tengfei Song, Wenming Zheng, Suyuan Liu, Yuan Zong, Zhen Cui and Yang Li, “Graph-Embedded Convolutional Neural Network for Image-based EEG Emotion Recognition[J],” IEEE Transactions on Emerging Topics in Computing, vol. 10, no. 3, pp. 1399-1413, 2022.

[9]    Cheng Lu, Yuan Zong, Wenming Zheng, Yang Li, Chuangao Tang, and Bjorn Schuller. “Domain Invariant Feature Learning for Speaker-independent Speech Emotion Recognition[J],” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.30, pp.2217-2230, 2022.

[10] Yang Li, Wenming Zheng, Yuan Zong, Zhen Cui, Tong Zhang, and Xiaoyan Zhou, “A Bi-hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition[J]”, IEEE Transactions on Affective Computing, vol. 12, no. 2, pp. 494-504, 2021. 【ESI Highly Cited Paper】

[11] Yang Li, Lei Wang, Wenming Zheng, Yuan Zong, Lei Qi, Zhen Cui, Tong Zhang, and Tengfei Song, “A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition[J]”, IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 2, pp. 354-367, 2021.【ESI Highly Cited Paper】

[12] Tong Zhang, Wenming Zheng, Zhen Cui, Yuan Zong, and Yang Li, “Spatio-Temporal Recurrent Neural Network for Emotion Recognition[J]”, IEEE Transactions on Cybernetics, vol. 49, no. 3, pp. 839-847, 2019. 【ESI Highly Cited Paper】

[13] Yang Li, Wenming Zheng, Zhen Cui, Tong Zhang, and Yuan Zong, “A Novel Neural Network Model based on Cerebral Hemispheric Asymmetry for EEG Emotion Recognition[C]”, International Joint Conference on Artificial Intelligence (IJCAI), pp. 1561-1567, 2018.

[14]    Yijin Zhou, Fu Li, Yang Li*, Youshuo Ji, Guangming Shi, Wenming Zheng, Lijian Zhang, Yuanfang Chen, Rui Cheng, “Progressive graph convolution network for EEG emotion recognition [J]”, Neurocomputing, vol. 544, pp 126262, 2023. (DOI: 10.1016/j.neucom.2023.126262)

[15]    Fu Li, Hongxin Li, Yang Li*, Hao Wu, Boxun Fu, Youshuo Ji, Chong Wang, and Guangming Shi, “Decoupling Representation Learning for Imbalanced Electroencephalography Classification in Rapid Serial Visual Presentation Task[J]”, Journal of Neural Engineering, vol. 19, no. 3, pp. 036011, 2022.

[16]    Youshuo Ji, Fu Li, Yang Li, Boxun Fu, Yi Niu, Yuanfang Chen and Guangming Shi, “Spatial-temporal Network for Fine-grained-level Emotion EEG Recognition[J]”, Journal of Neural Engineering, vol. 19, no. 3, pp. 036017, 2022

[17]    Boxun Fu, Fu Li, Yi Niu, Hao Wu, Yang Li, Guangming Shi. "Conditional Generative Adversarial Network For EEG-based Emotion Fine-Grained Estimation and Visualization[J]", Journal of Visual Communication and Image Representation, vol. 74, 2021.

[18]    Yang Li, Boxun Fu, Fu Li, Guangming Shi, Wenming Zheng, "A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition[J]", Neurocomputing, vol. 447, pp. 92-101, 2021.

[19]    Fu Li, Weibing Chao, Yang Li*, Boxun Fu, Youshuo Ji, Hao Wu and Guangming Shi, “Decoding Imagined Speech from EEG Signals using Hybrid-Scale Spatial-Temporal Dilated Convolution Network[J]”, Journal of Neural Engineering, vol. 18, no. 4, pp. 0460c4, 2021.

[20]    Yang Li, Wenming Zheng, Zhen Cui, and Yuan Zong, “EEG Emotion Recognition Based on Graph Regularized Sparse Linear Discriminant Analysis[J]”, Neural Processing Letters, vol. 49, no. 2, pp. 555-571, 2019.

[21]    Yang Li, Wenming Zheng, Zhen Cui, and Tong Zhang, “Face Recognition based on Recurrent Regression Neural Network[J]”, Neurocomputing, vol. 297, pp 50-58, 2018.

[22]    Chuangao Tang, Wenming Zheng, Jingwei Yan, Qiang Li, Yang Li, Tong Zhang, and Zhen Cui, “View-independent Facial Action Detection[C]”, IEEE 12th International Conference on Automatic Face & Gesture Recognition (FG), pp. 878-882, 2017.【Competition Champion】

[23] Yang Li, Wenming Zheng, Zhen Cui, and Xiaoyan Zhou, “A Novel Graph Regularized Sparse Linear Discriminant Analysis Model for EEG Emotion Recognition[C]”, International Conference on Neural Information Processing (ICONIP), pp. 175-182, 2016.