罗天健（1990-），男，湖北黄冈人，博士，讲师，硕士生导师。主要从事脑与认知科学、脑机接口、脑电信号分析理论及应用的研究。福建省人工智能学会理事，主持国家自然科学青年基金1项，福建省自然科学面上基金1项，福建省中青年教师教育科研项目1项。发表期刊、会议论文10余篇，其中SCI检索6篇。

1. 2012-2015，福州大学，计算机系，硕士

2. 2015-2019，厦门大学，智能科学与技术系，博士

3. 2019-今，福建师范大学，计算机与网络空间学院，讲师

1. 福建省人工智能学会理事

1. 国家自然科学青年基金：康复脑机接口的个性化反馈与增强方法研究，编号62106049，项目负责人。2022.01-2024.12

2. 福建省自然科学基金：康复脑机接口中脑电信号跨个体域迁移学习方法研究，编号2022J01132179，项目负责人。2022.03-2025.02

3. 福建省中青年教师教育科研项目：基于运动想象脑机接口的 EEG 时间序列分析应用研究，编号JAT190067，项目负责人。2020.01-2021.12

1. Li J, Wu S, Zhang X, Luo T*, Li R, Peng H. Cross-subject aesthetic preference recognition of Chinese dance posture using EEG. Cognitive Neurodynamics, 2022: 1-19.

2. Zhang X, Luo T*, Han T, Gao H. A novel performance degradation prognostics approach and its application on ball screw. Measurement, 2022, 195: 111184.

3. 郑成杰,肖国宝,罗天健*. 重叠时间切片改进深度神经网络的运动想象EEG模式识别. 计算机系统应用, 2022,31(05):52-64.

4. Luo T. A comparative survey of SSVEP recognition algorithms based on template matching of training trials. International Journal of Intelligent Computing and Cybernetics, 2022.

5. Luo T, Zhou C. Lateralized modulation brought by discrepancy speed ratios of left and right arm movements during human action observation: an EEG study. Multimedia Tools and Applications,  2022, 81(13): 17567-17594.

6. 罗天健,周昌乐.重叠特征策略与参数优化的运动想象脑电模式识别.模式识别与人工智能,2020,33(08):692-704.

7. Luo T, Fan Y, Chen L, Guo G, Zhou C. EEG signal reconstruction using a generative adversarial network with Wasserstein distance and temporal-spatial-frequency loss. Frontiers in Neuroinformatics, 2020, 14:15.

8. Luo T, Lv J, Chao F, Zhou C. Effect of different movement speed modes on human action observation: an EEG study. Frontiers in neuroscience, 2018, 12: 219.

9. Luo T, Fan Y, Lv J. Deep reinforcement learning from error-related potentials via an EEG-based brain-computer interface, In: Proceedings of the 12th IEEE International Conference on Bioinformatics and Biomedicine (BIBM-18). IEEE, 2018: 697-701.

10. Luo T, Chao F, Zhou C. Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network. BMC Bioinformatics, 2018, 19(1): 344.