Complete Publication List

Google Scholar Profile

Brain-Computer Interface:

1. D. Wu, B-L Lu, B. Hu* and Z. Zeng*, "Affective Brain-Computer Interfaces (aBCIs): A Tutorial," Proceedings of the IEEE, 111(10):1314-1332, 2023.

2. L. Meng, X. Jiang, X. Chen, W. Liu, H. Luo and D. Wu*, "Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces," Information Fusion, 107:102316, 2024. (Python)

3. L. Meng, X. Jiang, J. Huang, H. Luo and D. Wu*, "User Identity Protection in EEG-based Brain-Computer Interfaces," IEEE Trans. on Neural Systems and Rehabilitation Engineering, 31:3576-3586, 2023. (Python)

4. L. Meng, X. Jiang and D. Wu*, "Adversarial Robustness Benchmark for EEG-Based Brain-Computer Interfaces," Future Generation Computer Systems, 143:231-247, 2023. (Python)

5. R. Bian, H. Wu, B. Liu and D. Wu*, "Small Data Least-Squares Transformation (sd-LST) for Fast Calibration of SSVEP-based BCIs," IEEE Trans. on Neural Systems and Rehabilitation Engineering, 31:446-455, 2023. (Python)

6. D. Wu, J. Xu, W. Fang, Y. Zhang, L. Yang, X. Xu*, H. Luo* and X. Yu*, "Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review," National Science Open, 1:20220023, 2022.

7. D. Wu*, X. Jiang, R. Peng, "Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Tutorial," Neural Networks, 153:235-253, 2022. (Matlab)

8. R. Bian, L. Meng and D. Wu*, "SSVEP-Based Brain-Computer Interfaces Are Vulnerable to Square Wave Attacks," Science China Information Sciences, 65(4):140406, 2022. (Python)

9. D. Wu*, Y. Xu and B.-L. Lu, "Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016," IEEE Trans. on Cognitive and Developmental Systems, 14(1):4-19, 2022. (ESI Highly Cited Paper)

10. X. Zhang, D. Wu*, L. Ding*, H. Luo, C-T Lin, T-P Jung and R. Chavarriaga, "Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellers," National Science Review, 8(4), 2021. (Python; TechXplore; TechXplore2)

11. Z. Liu, L. Meng, X. Zhang, W. Fang, D. Wu*, "Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs," Journal of Neural Engineering, 8:0460a4, 2021. (Python)

12. W. Zhang and D. Wu*, "Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces," IEEE Trans. on Neural Systems and Rehabilitation Engineering, 28(5):1117-1127, 2020. (Python; 世界机器人大会)

13. H. He and D. Wu*, "Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach," IEEE Trans. on Neural Systems and Rehabilitation Engineering, 28(5):1091-1108, 2020. (Matlab; IEEE Brain)

14. H. He and D. Wu*, "Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach," IEEE Trans. on Biomedical Engineering, 67(2):399-410, 2020. (ESI Highly Cited Paper; Matlab; IEEE Brain)

15. Y. Cui, Y. Xu and D. Wu*, "EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training," IEEE Trans. on Neural Systems and Rehabilitation Engineering, 27(11):2263-2273, 2019. (IEEE TNSRE Cover Article; Python) 

16. X. Zhang and D. Wu*, "On the Vulnerability of CNN Classifiers in EEG-Based BCIs," IEEE Trans. on Neural Systems and Rehabilitation Engineering, 27(5):814-825, 2019. (Python)

17. D. Wu*, J-T King, C-C Chuang, C-T Lin and T-P Jung, "Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)," IEEE Trans. on Fuzzy Systems, 26(2):771-781, 2018.

18. D. Wu*, B. J. Lance, V. J. Lawhern, S. Gordon, T-P Jung and C-T Lin, “EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features”, IEEE Trans. on Neural Systems and Rehabilitation Engineering, 25(11):2157-2168, 2017. 

19. D. Wu, V. Lawhern, S. Gordon, B. Lance and C-T Lin, "Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)," IEEE Trans. on Fuzzy Systems, 25(6):1522-1535, 2017.

20. D. Wu*, "Online and Offline Domain Adaptation for Reducing BCI Calibration Effort," IEEE Trans. on Human-Machine Systems, 47(4): 550-563, 2017. (ESI Highly Cited Paper)

21. D. Wu, V. Lawhern, D. Hairston and B. Lance, "Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization," IEEE Trans. on Neural Systems and Rehabilitation Engineering, 24(11):1125-1137, 2016.

Affective Computing:

1. Y. Xu, X. Jiang and D. Wu*, "Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition," IEEE Trans. on Affective Computing, 2024, in press. (Python)

2. Y. Xu, Y. Cui, X. Jiang, Y. Yin, J. Ding, L. Li and D. Wu*, "Inconsistency-Based Multi-Task Cooperative Learning for Dimensional Emotion Recognition," IEEE Trans. on Affective Computing, 13(4):2017-2027, 2022. (Python)

3. D. Wu* and J. Huang, "Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression," IEEE Trans. on Affective Computing, 13(1):16-27, 2022.

4. S. Li, Y. Xu, H. Wu, D. Wu*, Y. Yin, J. Cao and J. Ding, "Facial Expression Recognition in-the-wild with Deep Pre-trained Models," European Conference on Computer Vision (ECCV) ABAW Workshop, Tel Aviv, Israel, October 2022.  (Python)

5. 权学良, 曾志刚, 蒋建华, 张亚倩, 吕宝粮, 伍冬睿*, 基于生理信号的情感计算研究综述. 自动化学报, 47(8):1769−1784, 2021.

6. D. Wu, C. Courtney, B. Lance, S. Narayanan, M. Dawson, K. Oie, and T.D. Parsons, “Optimal Arousal Identification and Classification for Affective Computing: Virtual Reality Stroop Task,” IEEE Trans. on Affective Computing, 1(2):109-118, 2010. (Top Accessed Article; IEEE Trans. on Affective Computing Most Influential Paper Award Finalist)

Machine Learning:

1. C. Zhao, D. Wu*, J. Huang, Y. Yuan, H-T Zhang, R. Peng and Z. Shi, "BoostTree and BoostForest for Ensemble Learning," IEEE Trans. on Pattern Analysis and Machine Intelligence, 45(7):8110-8126, 2023. (Python)

2. W. Zhang, L. Deng, L. Zhang and D. Wu*, "A Survey on Negative Transfer," IEEE/CAA Journal of Automatica Sinica, 10(2):305-329, 2023. (Python)

3. Y. Cui, D. Wu*, Y. Xu and R. Peng, "Layer Normalization for TSK Fuzzy System Optimization in Regression Problems," IEEE Trans. on Fuzzy Systems, 31(1):254-264, 2023. (Python)

4. X. Zhang, H. Xiong and D. Wu, "Rethink the Connections among Generalization, Memorization, and the Spectral Bias of DNNs," Int'l Joint Conf. on Artificial Intelligence (IJCAI), Montreal, Canada, August 2021.

5. 刘子昂, 蒋雪, 伍冬睿*, "基于池的无监督线性回归主动学习," 自动化学报, 47(12):2771-2783, 2021. (Matlab)

6. Z. Shi, D. Wu*, C. Guo, C. Zhao, Y. Cui and F-Y Wang*, "FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief", Information Sciences, 574:490:504, 2021. (Matlab)

7. Z. Liu, X. Jiang, H. Luo, W. Fang, J. Liu and D. Wu*, "Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)," Pattern Recognition Letters, 142:11-19, 2021. (Matlab)

8. X. Zhang and D. Wu*, "Empirical Studies on the Properties of Linear Regions in Deep Neural Networks," Int'l. Conf. on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020.

9. Y. Cui, D. Wu* and J. Huang*, "Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization," IEEE Trans. on Fuzzy Systems, 28(12):3065-3075, 2020. (Matlab; Python)

10. D. Wu*, Y. Yuan, J. Huang and Y. Tan*, "Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA)," IEEE Trans. on Fuzzy Systems, 28(5):1003-1015, 2020. (Matlab)

11. D. Wu* and J.M. Mendel, "Patch Learning," IEEE Trans. on Fuzzy Systems, 28(9):1996-2008, 2020. (Matlab; IEEE CIS Publication Spotlight)

12. D. Wu, C-T Lin, J. Huang* and Z. Zeng*, "On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression," IEEE Trans. on Fuzzy Systems, 28(10):2570-2580, 2020.

13. D. Wu* and X. Tan, "Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization," IEEE Trans. on Fuzzy Systems, 28(6):1050-1061, 2020. (Matlab; IEEE CIS Publication Spotlight)

14. D. Wu*, C-T Lin and J. Huang*, "Active Learning for Regression Using Greedy Sampling," Information Sciences, 474:90-105, 2019. (Matlab)

15. D. Wu, "Pool-based sequential active learning for regression," IEEE Trans. on Neural Networks and Learning Systems, 30(5): 1348-1359, 2019. (Matlab)

Smart Healthcare:

1. Z. Du, R. Peng, W. Liu, W. Li* and D. Wu*, "Mixture of Experts for EEG-Based Seizure Subtype Classification," IEEE Trans. on Neural Systems and Rehabilitation Engineering, 2023, in press. (Python)

2. Z. Wang, W. Zhang, S. Li, X. Chen and D. Wu*, "Unsupervised Domain Adaptation for Cross-Patient Seizure Classification," Journal of Neural Engineering, 20(6):066002, 2023. (Python)

3. R. Peng, C. Zhao, J. Jiang, G. Kuang, Y. Cui, Y. Xu, H. Du, J. Shao*, and D. Wu*, "TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification," IEEE Trans. on Neural Systems and Rehabilitation Engineering, 30:2567-2576, 2022. (Python)

4. 彭睿旻, 江军, 匡光涛, 杜浩, 伍冬睿*, 邵剑波. 基于EEG的癫痫自动检测: 综述与展望. 自动化学报, 48(2):335-350, 2022.

5. X. Yang, Y. Zhang, B. Lo, D. Wu, H. Liao an Y-T Zhang, "DBAN: Adversarial Network with Multi-Scale Features for Cardiac MRI Segmentation," IEEE Journal of Biomedical and Health Informatics, 25(6):2018-2028, 2021.

6. X. Song, P. Qian, J. Zheng, Y. Jiang, K. Xia, B. Traughber, D. Wu and R. F. Muzic, "mDixon-Based Synthetic CT Generation via Transfer and Patch Learning," Pattern Recognition Letters, 138: 51-59, 2020.

7. Y. Jiang, X. Gu, D. Wu, W. Hang, J. Xue, S. Qiu and C-T Lin, "A Novel Negative-Transfer-Resistant Fuzzy Clustering Model with a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation," IEEE/ACM Trans. on Computational Biology and Bioinformatics, 18(1): 40-52, 2021.

8. X. Tian, Z. Deng, K-S Choi, D. Wu, B. Qin, J. Wan, H. Shen and S. Wang, "Deep multi-view feature learning for epileptic seizure detection," IEEE Trans. on Neural Systems and Rehabilitation Engineering, 27(10):1962-1972, 2019.

9. Y. Jiang, D. Wu, Z. Deng, P. Qian, J. Wang, G. Wang, F-L Chung, K-S Choi and S. Wang, "Seizure Classification from EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System," IEEE Trans. on Neural Systems and Rehabilitation Engineering, 25(12):2270-2284, 2017.

Intelligent Control:

1. D. Wu*, R. Peng and J.M. Mendel, “Type-1 and interval type-2 fuzzy systems,” IEEE Computational Intelligence Magazine, 18(1):81-83, 2023.

2. 伍冬睿*，曾志刚，莫红，王飞跃，“区间二型模糊集和模糊系统: 综述与展望," 自动化学报, 46(8):1539-1556, 2020.

3. D. Wu* and J.M. Mendel, "Recommendations on Designing Practical Interval Type-2 Fuzzy Systems", Engineering Applications of Artificial Intelligence, 95:182-193, 2019.

4. J. Huang, M. Ri, D. Wu* and S. Ri, "Interval Type-2 Fuzzy Logic Modeling and Control of a Mobile Two-Wheeled Inverted Pendulum," IEEE Trans. on Fuzzy Systems, 26(4):2030-2036, 2018.

5. D. Wu*, “Approaches for Reducing the Computational Cost of Interval Type-2 Fuzzy Logic Controllers: Overview and Comparison,” IEEE Trans. on Fuzzy Systems, 21(1):80-99, 2013. (ESI Highly Cited Paper)

6. D. Wu*, “On the Fundamental Differences between Interval Type-2 and Type-1 Fuzzy Logic Controllers,” IEEE Trans. on Fuzzy Systems, 20(5):832-848, 2012. (IEEE CIS Publication Spotlight)

7. D. Wu and J. M. Mendel, “On the Continuity of Type-1 and Interval Type-2 Fuzzy Logic Systems,” IEEE Trans. on Fuzzy Systems, 19(1):179-192, 2011. (2014 IEEE TFS Outstanding Paper Award; IEEE CIS Publication Spotlight)

8. D. Wu and J. M. Mendel, “Enhance Karnik-Mendel Algorithms,” IEEE Trans. on Fuzzy Systems, 17:923-934, 2009. (ESI Highly Cited Paper; Ranked 12th among all 1,288 SCI papers published worldwide on type-2 fuzzy systems in 1997-2017, according to "A Bibliometric Overview of the Field of Type-2 Fuzzy Sets and Systems," IEEE Computational Intelligence Magazine, 15(1), pp. 89-98, 2020; Available in Matlab Fuzzy Logic Toolbox)

9. D. Wu and W. W. Tan, “Genetic Learning and Performance Evaluation of Type-2 Fuzzy Logic Controllers,” Engineering Applications of Artificial Intelligence, 19(8):829-841, 2006. (Ranked 13th among all 2,960 papers published in EAAI in 1988-2018, according to "Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988–2018)," EAAI, 85, pp. 517–532, 2019)

10. D. Wu and W. W. Tan, “A Simplified Type-2 Fuzzy Controller for Real-Time Control,” ISA Trans., 15(4):503-516, 2006.

11. D. Wu and W. W. Tan, “Type-2 FLS Modeling Capability Analysis,” IEEE Int'l Conf. on Fuzzy Systems, Reno, USA, May 2005. (Best Student Paper Award)
    
    

Perceptual Computing & Decision Making:

1. J. M. Mendel and D. Wu, “Perceptual Computing: Aiding People in Making Subjective Judgments,” Wiley-IEEE Press, April 2010. (Matlab code) (Book Review)(Google Books)

2. D. Wu* and J. M. Mendel, "Similarity Measures for Closed General Type-2 Fuzzy Sets: Overview, Comparisons, and a Geometric Approach," IEEE Trans. on Fuzzy Systems, 27(3):515-526, 2019.

3. D. Wu, H-T Zhang* and J. Huang*, "A Constrained Representation Theorem for Well-Shaped Interval Type-2 Fuzzy Sets, and the Corresponding Constrained Uncertainty Measures," IEEE Trans. on Fuzzy Systems, 27(6):1237-1251, 2019. (IEEE CIS Publication Spotlight)

4. D. Wu*, “A Reconstruction Decoder for Computing with Words,” Information Sciences, 255:1-15, 2014.

5. D. Wu and J. M. Mendel, “Linguistic Summarization Using IF-THEN Rules and Interval Type-2 Fuzzy Sets,” IEEE Trans. on Fuzzy Systems, 19(1):136-151, 2011.

6. D. Wu and J.M. Mendel, “Computing With Words for Hierarchical Decision Making Applied to Evaluating a Weapon System,” IEEE Trans. on Fuzzy Systems, 18(3):441-460, 2010.

7. D. Wu and J. M. Mendel, “Perceptual reasoning for perceptual computing: A similarity-based approach,” IEEE Trans. on Fuzzy Systems, 17(6):1397-1411, 2009.

8. D. Wu and J. M. Mendel, “A Comparative Study of Ranking Methods, Similarity Measures and Uncertainty Measures for Interval Type-2 Fuzzy Sets,” Information Sciences, 179(8):1169-1192, 2009. (ESI Highly Cited Paper; Top 25 Hottest Article)

9. D. Wu and J. M. Mendel, “Aggregation Using the Linguistic Weighted Average and Interval Type-2 Fuzzy Sets,” IEEE Trans. on Fuzzy Systems, 15(6):1145-1161, 2007.

10. D. Wu and J. M. Mendel, “Uncertainty Measures for Interval Type-2 Fuzzy Sets,” Information Sciences, 177:5378-5393, 2007.
    
    

Theses:

1. D. Wu, “Intelligent Systems for Decision Support,” PhD Dissertation, Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, March 2009. (2009 Viterbi Best Dissertation Nomination; 2012 IEEE Computational Intelligence Society Outstanding Dissertation Award)

Patents:

1. A. Kumar, B. Ellis, Z. Wan, C. Pierce, M. Dokucu, D. Wu and S. Balram, Dynamic monitoring, diagnosis, and control of cooling tower systems, WO2015012832, 1/29/2015.

2. S. Gustfason and D. Wu, Influencer analyzer platform for social and traditional media document authors, US20150348216, 12/3/2015.

3. J. Reimann, C. Johnson, D. Wu, S. Evans, R. Cheinhample, and A. Pandey, System and method using generative model to supplement incomplete industrial plant information, US20160004794, 1/7/2016.

4. A. Can, E. Bas, D. Wu, J. Yu, and L. Wahrmund, Expert guided knowledge acquisition system for analyzing seismic data, WO2017152119, 8/15/2017.

5. X. Gui, B. Shi, H. Liu and D. Wu, Target Positioning And Tracking System, Device, And Positioning And Tracking Method, WO2017084240, 5/1/2017.

6. 伍冬睿，石振华，一种用于恒河猴眼动决策解码的多视图学习方法和系统，201910586165.4， 2020-07-10

7. 伍冬睿，谭显烽，一种适用于云计算系统的多任务处理方法，201811434588.6， 2020-07-10

8. 伍冬睿，孟璐斌，一种基于EEG的脑机接口回归系统白盒目标攻击方法，201910896360.7， 2020-08-04

9. 伍冬睿，张潇，一种针对以卷积神经网络为基础的EEG脑机接口的攻击方法，201811543220.3，2020-11-10

10. 伍冬睿，何赫，一种用于脑机接口校准的异构标签空间迁移学习方法，201911100099.1，2021-08-04

11. 伍冬睿，刘子涵，一种胚胎时序图像中的胚胎发育阶段识别方法，2019106052820, 1/11/2022

12. 伍冬睿，蒋雪，一种脑机接口系统黑盒攻击方法，2019109826823, 1/12/2022

13. 伍冬睿，刘子昂，一种用于语音情感计算的无监督主动学习方法，201910999055.0, 1/29/2022

14. 伍冬睿，夏坤，基于欧氏对齐和Procrustes分析的EEG分类的迁移学习方法和系统，202010578377.0，2022.2.18

Talks:

1. 脑机接口中的机器学习, 5/12/2020.

2. Affective Computing, 12/6/2011.