Paper Publications

• [1]近年来在领域内旗舰期刊及会议共发表24篇，成果入选亮点文章。主要代表性论文如下（*表示通讯作者）.
• [2][1] L. Wu(吴乐), X. Zhang*, K. Wang, X. Chen, and X. Chen, “Improved High-Density Myoelectric Pattern Recognition Control Against Electrode Shift Using Data Augmentation and Dilated Convolutional Neural Network,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 12, pp. 2637-2646, 2020. （IF：4.528，康复医学领域旗舰期刊，被期刊评为Featured Article（亮点文章））.
• [3][2] L. Wu(吴乐), A. Liu*, X. Zhang, X. Chen, and X. Chen, “Electrode Shift Robust CNN for High-Density Myoelectric Pattern Recognition Control,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10, 2022. （IF：5.332，在跨试次肌电识别公开数据集Hyser达到最优性能）.
• [4][3] L. Wu(吴乐), X. Zhang, X. Zhang, X. Chen, and X. Chen*, “Metric learning for novel motion rejection in high-density myoelectric pattern recognition,” Knowledge-Based Systems, vol. 227, pp. 107165, 2021. （IF：8.139，计算机科学领域1区Top期刊）.
• [5][4] L. Wu(吴乐), A. Liu*, X. Zhang, X. Chen, and X. Chen, “Unknown Motion Rejection in Myoelectric Pattern Recognition Using Convolutional Prototype Network,” IEEE Sensors Journal, vol. 22, no. 5, pp. 4305-4314, 2022. （IF：4.325，仪器仪表领域Top期刊）.
• [6][5] L. Wu(吴乐), X. Chen, X. Chen, and X. Zhang*, “Rejecting Novel Motions in High-Density Myoelectric Pattern Recognition Using Hybrid Neural Networks,” Frontiers in Neurorobotics, vol. 16, 2022. （IF：3.493）.
• [7][6] L. Wu(吴乐), X. Zhang*, X. Chen, and X. Chen, "Visualized evidences for detecting novelty in myoelectric pattern recognition using 3D convolutional neural networks." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp. 2641-2644. （生物医学工程领域旗舰会议）.
• [8][7] S. Duan, L. Wu*(吴乐，共同通讯作者), B. Xue, A. Liu, R. Qian, and X. Chen*, “A Hybrid Multimodal Fusion Framework for sEMG-ACC-Based Hand Gesture Recognition,” IEEE Sensors Journal, 2023. （IF：4.325，仪器仪表领域Top期刊，在公开数据集NinaPro肌电识别性能达到最优）.
• [9][8] B. Xue, L. Wu*(吴乐，共同通讯作者), A. Liu, X. Zhang, X. Chen, and X. Chen*, “Reduce the User Burden of Multiuser Myoelectric Interface via Few-Shot Domain Adaptation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023. （IF：4.528，康复医学领域旗舰期刊，在数据集CapgMyo域适应结果最优）.
• [10][9] X. Zhang, L. Wu*(吴乐，共同通讯作者), X. Zhang, X. Chen, C. Li, and X. Chen*, “Multi-source domain generalization and adaptation towards cross-subject myoelectric pattern recognition,” Journal of Neural Engineering, 2023. （IF：5.043，神经科学领域旗舰期刊，针对6类手势肌电识别泛化性结果达到最优）.