Bao-Liang Lu

Ph.D., IEEE Fellow, Professor, Executive Dean, Directors
Center for Brain-like Computing and Machine Intelligence
Department of Computer Science and Engineering
Shanghai Jiao Tong University
Key Laboratory of Shanghai Commission for Intelligent Interaction and Cognitive Engineering
Shanghai Jiao Tong University
Center for Brain-Machine Interface and Neuromodulation, RuiJin Hospital, Shanghai Jiao Tong University School of Medicine
RuiJin-MiHoYo Laboratory, RuiJin Hospital, Shanghai Jiao Tong University School of Medicine

bllu AT sjtu DOT edu DOT cn
Phone.: +86-21-34205422
Office: Room 431, Dian Xin Building 3
Address: 800 Dong Chuan Road Shanghai 200240, China

Research Interest

Brain-like Computing, Neural Networks, Deep Learning, Emotion AI, Affective Brain-Computer Interface.

Education Background

I received the B.S. degree in instrument and control engineering from Qingdao University of Science and Technology, China, in 1982, the M. S. degree in computer science and engineering from Northwestern Polytechnical University, Xi'an, China, in 1989, and the Dr. Eng. degree in electrical engineering from Kyoto University, Kyoto, Japan, in 1994.

Work Experience

From 1982 to 1986, I was with the Qingdao University of Science and Technology. From April 1994 to March 1999, I was a Frontier Researcher at the Bio-Mimetic Control Research Center, the Institute of Physical and Chemical Research (RIKEN), Nagoya, Japan. From April 1999 to August 2002, I was a Research Scientist at the RIKEN Brain Science Institute, Wako, Japan. Since August 2002, I have been a full professor at the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China.

Teaching

  1. Neural Network Theory and Applications
  2. Parallel Machine Learning with Application to Large-Scale Data Mining

Awards

  1. 2012 Asia Pacific Neural Network Assembly (APNNA) Excellent Service Award
  2. 2018 IEEE Transactions on Autonomous Mental Development Outstanding Paper Award
  3. 2020 First Prize, Wu Wen Jun AI Science & Technology Award
  4. 2021 Best of IEEE Transactions on Affective Computing Paper Collection
  5. ACM Multimedia 2022 Top Paper Award
  6. 2022 Asia Pacific Neural Network Society (APNNS) Outstanding Achievement Award

Editorial Board of

  1. IEEE Transactions on Affective Computing 2022-
  2. Journal of Neural Engineering 2021-
  3. Chinese Journal of Intelligent Science and Technology 2019-
  4. Pattern Recognition and Artificial Intelligence (in Chinese) 2017-
  5. IEEE Transactions on Cognitive and Developmental Systems 2016-

Major Grants

  1. Investigating Age, Gender, and Cultural Differences in Emotion Recognition Using EEG and Eye Movement Signals
    Principal Investigator: Bao-Liang Lu
    Grant Number: NSFC 61976135
    Agency: National Science Foundation of China
    Period: Jan. 1, 2020 to Dec. 31, 2023
  2. A Study on the Discrimination Ability, Representation Characteristics and Stability of EEG and Eye Movement Signals for Multimodal Emotion Recognition
    Principal Investigator: Bao-Liang Lu
    Grant Number: NSFC 61673266
    Agency: National Science Foundation of China
    Period: Jan. 1, 2017 to Dec. 31, 2020
  3. A Study on Visual Cognition Coding and Information Integration Mechanism of the Brain
    Principal Investigator: Bao-Liang Lu
    Grant Number: 2013CB329401
    Agency: National Basic Research Program (973) of China
    Period: Jan. 1, 2013 to Dec. 31, 2017
  4. A Study on Key Techniques for Fatigue Monitoring by Using Forehead EOG
    Principal Investigator: Bao-Liang Lu
    Grant Number: NSFC 61272248
    Agency: National Science Foundation of China
    Period: Jan. 1, 2013 to Dec. 31, 2016
  5. A Driving Fatigue Detection System by Combining EOG and EEG Signals
    Principal Investigator: Bao-Liang Lu
    Grant Number: 13511500200
    Agency: Science and Technology Commission of Shanghai Municipality
    Period: July. 1, 2013 to June 30, 2015
  6. Dry-electrode-based Wireless Wearable EEG-cap and Its Application to Drivers' Vigilance Monitor System
    Principal Investigator: Bao-Liang Lu
    Grant Number: 09511502400
    Agency: Science and Technology Commission of Shanghai Municipality
    Period: July. 1, 2009 to June 30, 2011
  7. The Basic Mechanism Research of Visual Information Processing
    Principal Investigator: Bao-Liang Lu
    Grant Number: 2009CB320901
    Agency: National Basic Research Program (973) of China
    Period: Jan. 1, 2009 to Dec. 31, 2013
  8. EEG Based Vigilance Estimation and Prediction for Drivers
    Principal Investigator: Bao-Liang Lu
    Grant Number: NSFC 90820018
    Agency: National Science Foundation of China
    Period: Jan. 1, 2009 to Dec. 31, 2011
  9. Integrated platform for analysis of metagenomic and metabonomic data and study on metabolic disease
    Principal Investigator: Bao-Liang Lu
    Grant Number: NSFC 2008AA02Z315
    Agency: National High-Tech Research Program (863) of China
    Period: Jan. 1, 2008 to Dec. 31, 2010.
  10. Learning algorithm for Large-scale, multi-label, and imbalanced classification problems
    Principal Investigator: Bao-Liang Lu
    Grant Number: NSFC 60773090
    Agency: National Science Foundation of China
    Period: Jan. 1, 2008 to Dec. 31, 2010.
  11. Task decomposition and module combination for massively parallel pattern classifiers
    Principal Investigator: Bao-Liang Lu
    Grant Number: NSFC 60473040
    Agency: National Science Foundation of China
    Period: Jan. 1, 2005 to Dec. 31, 2007.
  12. Incremental learning model
    Principal Investigator: Bao-Liang Lu
    Grant Number: NSFC 60375022
    Agency: National Science Foundation of China
    Period: Jan. 1, 2004 to Dec. 31, 2006.

Publication

2024

  1. Wei-Bang Jiang, Li-Ming Zhao, Bao-Liang Lu, Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI. In The Twelfth International Conference on Learning Representations, 2024. [pdf]
  2. Wei-Bang Jiang*, Ziyi Li*, Wei-Long Zheng, Bao-Liang Lu, Functional Emotion Transformer for EEG-Assisted Cross-Modal Emotion Recognition, Proc. of ICASSP 2024. (*contributed equally as joint first authors) [pdf]
  3. Ziyi Li, Li-Ming Zhao, Wei-Long Zheng, Bao-Liang Lu, Temporal-Spatial Prediction: Pre-Training on Diverse Datasets for EEG Classification, Proc. of ICASSP 2024. [pdf]
  4. Pengxuan Gao, Tianyu Liu, Jia-Wen Liu, Bao-Liang Lu, Wei-Long Zheng, Multimodal Multi-View Spectral-Spatial-Temporal Masked Autoencoder for Self-Supervised Emotion Recognition, Proc. of ICASSP 2024. [pdf]
  5. Yu-Ting Lan, Wei-Bang Jiang, Wei-Long Zheng, Bao-Liang Lu, CEMOAE: A Dynamic Autoencoder with Masked Channel Modeling for Robust EEG-Based Emotion Recognition, Proc. of ICASSP 2024. [pdf]
  6. Wei-Bang Jiang*, Yu-Ting Lan*, Bao-Liang Lu, REmoNet: Reducing Emotional Label Noise via Multi-regularized Self-supervision. Proceedings of the 32nd ACM International Conference on Multimedia (ACM MM 2024). (*contributed equally as joint first authors) [pdf]
  7. Wei-Bang Jiang*, Xuan-Hao Liu*, Wei-Long Zheng, Bao-Liang Lu, SEED-VII: A Multimodal Dataset of Six Basic Emotions with Continuous Labels for Emotion Recognition, accepted by IEEE Transactions on Affective Computing. (*contributed equally as joint first authors)
  8. Xuan-Hao Liu* , Yan-Kai Liu*, Yansen Wang , Kan Ren , Hanwen Shi, Zilong Wang, Dongsheng Li, Bao-Liang Lu, Wei-Long Zheng, EEG2Video: Towards Decoding Dynamic Visual Perception from EEG Signals, to be appeared in NeurIPS 2024. (*contributed equally as joint first authors)
  9. Shi-Heng Tian, Ziyi Li, Bao-Liang Lu, Wei-Long Zheng, Multi-View Fusion Transformer For Emotion Recognition Under Sleep Deprivation, to be appeared in ICONIP 2024.
  10. Rong-Fei Gu, Yi-dong Zhao, Li-Ming Zhao, Wei-Long Zheng, Bao-Liang Lu, A Multi-modal Emotion Recognition Model based on Continuously Labeled EEG Signals, to be appeared in ICONIP 2024.
  11. Tian-Fang Ma, Lu-Yu Liu, Li-Ming Zhao, Dan Peng, Yong Lu, Wei-Long Zheng, Bao-Liang Lu, Detecting Major Depression Disorder with Multiview Eye Movement Features in a Novel Oil Painting Paradigm. IJCNN 2024: 1-8. [pdf]
  12. Tian-Hua Li, Tian-Fang Ma, Dan Peng, Wei-Long Zheng, Bao-Liang Lu, Focused State Recognition Using EEG with Eye Movement-Assisted Annotation. CoRR abs/2407.09508 (2024)
  13. Yifei Yang, Runhan Shi, Zuchao Li, Shu Jiang, Bao-Liang Lu, Yang Yang, Hai Zhao, BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction. CoRR abs/2408.10285 (2024)
  14. Wei-Bang Jiang, Yansen Wang, Bao-Liang Lu, Dongsheng Li, NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals. CoRR abs/2409.00101 (2024)
  15. Xuan-Hao Liu, Xinhao Song, Dexuan He, Bao-Liang Lu, Wei-Long Zheng, Professor X: Manipulating EEG BCI with Invisible and Robust Backdoor Attack. CoRR abs/2409.20158 (2024)

2023

  1. Wei-Bang Jiang, Xu Yan, Wei-Long Zheng, Bao-Liang Lu, Elastic Graph Transformer Networks for EEG-based Emotion Recognition, Proc. of ICASSP 2023 [pdf]
  2. Cheng Fei, Rui Li, Li-Ming Zhao, Wei-Long Zheng and Bao-Liang Lu, EEG-Eye Movements Cross-Modal Decision Confidence Measurement with Generative Adversarial Networks, Proc. of IEEE NER 2023 [pdf]
  3. Yu-Ting Lan, Ze-Chen Li, Dan Peng, Wei-Long Zheng, and Bao-Liang Lu，Identifying Artistic Expertise Difference in Emotion Recognition in Response to Oil Paintings, Proc. of IEEE NER 2023 [pdf]
  4. Wei-Bang Jiang, Xuan-Hao Liu, Wei-Long Zheng, Bao-Liang Lu, Multimodal Adaptive Emotion Transformer with Flexible Modality Inputs on A Novel Dataset with Continuous Labels, Proceedings of the 31th ACM International Conference on Multimedia (ACM MM 2023) [pdf]
  5. Rong-Fei Gu, Li-Ming Zhao, Wei-Long Zheng, Bao-Liang Lu, Tagging Continuous Labels for EEG-based Emotion Classification, EMBC 2023 [pdf]
  6. Dan Peng, Wei Liu, Yun Luo, Ziyu Mao, Wei-Long Zheng, Bao-Liang Lu, Deep Depression Detection with Resting-State and Cognitive-Task EEG, EMBC 2023 [pdf]
  7. Yu-Ting Lan, Dan Peng, Wei Liu, Yun Luo, Ziyu Mao, Wei-Long Zheng, Bao-Liang Lu, Investigating Emotion EEG Patterns for Depression Detection with Attentive Simple Graph Convolutional Network, EMBC 2023 [pdf]
  8. Luyu Liu, Dan Peng, Wei-Long Zheng, Bao-Liang Lu, Objective Depression Detection Using EEG and Eye Movement Signals Induced by Oil Paintings, EMBC 2023 [pdf]
  9. Xuan-Hao Liu, Wei-Bang Jiang, Wei-Long Zheng, Bao-Liang Lu, Two-Stream Spectral-Temporal Denoising Network for End-to-end Robust EEG-based Emotion Recognition. ICONIP 2023 [pdf]
  10. Jian-Ming Zhang, Jiawen Liu, Ziyi Li, Tian-Fang Ma, Yiting Wang, Wei-Long Zheng, Bao-Liang Lu, Naturalistic Emotion Recognition Using EEG and Eye Movements. ICONIP 2023 [pdf]
  11. Zhong-Wei Jin, Jia-Wen Liu, Wei-Long Zheng, Bao-Liang Lu, DAformer: Transformer with Domain Adversarial Adaptation for EEG-based Emotion Recognition with Live-Oil Paintings. ICONIP 2023 [pdf]
  12. Yong Peng, Keding Chen, Feiping Nie, Bao-Liang Lu, Wanzeng Kong, Two-Dimensional Embedded Fuzzy Data Clustering. IEEE Trans. Emerg. Top. Comput. Intell. 7(4): 1263-1275 (2023)
  13. Yong Peng, Honggang Liu, Wanzeng Kong, Feiping Nie, Bao-Liang Lu, Andrzej Cichocki, Joint EEG Feature Transfer and Semisupervised Cross-Subject Emotion Recognition. IEEE Trans. Ind. Informatics 19(7): 8104-8115 (2023)
  14. Yu-Ting Lan, Kan Ren, Yansen Wang, Wei-Long Zheng, Dongsheng Li, Bao-Liang Lu, Lili Qiu, Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals. CoRR abs/2308.02510 (2023)
  15. Yong Peng, Wenhua Huang, Wanzeng Kong, Feiping Nie, Bao-Liang Lu, JGSED: An End-to-End Spectral Clustering Model for Joint Graph Construction, Spectral Embedding and Discretization. IEEE Transactions on Emerging Topics in Computational Intelligence, 2023 [pdf]
  16. Dongrui Wu, Bao-Liang Lu, Bin Hu, Zhigang Zeng, Affective Brain–Computer Interfaces (aBCIs): A Tutorial. Proceedings of the IEEE, 2023 [pdf]
  17. Rong-Fei Gu, Rui Li, Wei-Long Zheng, Bao-Liang Lu, Cross-Subject Decision Confidence Estimation from EEG Signals Using Spectral-Spatial-Temporal Adaptive GCN with Domain Adaptation, IJCNN 2023 [pdf]
  18. Jing-Yi Liu, Jia-Wen Liu, Wei-Long Zheng, Bao-Liang Lu: Transformer-Based Domain Adaptation for Multi-Modal Emotion Recognition in Response to Game Animation Videos. BIBM 2023: 879-884 [pdf]

2022

  1. Rui Li, Yi-Ting Wang, Wei-Long Zheng, Bao-Liang Lu, A Multi-view Spectral-Spatial-Temporal Masked Autoencoder for Decoding Emotions with Self-supervised Learning, Proceedings of the 30th ACM International Conference on Multimedia (ACM MM 2022) [pdf]
  2. Wei Liu, Wei-Long Zheng, Ziyi Li, Si-Yuan Wu, Lu Gan, Bao-Liang Lu, Identifying similarities and differences in emotion recognition with EEG and eye movements among Chinese, German, and French People, Journal of Neural Engineering, vol. 19 (2022) 026012, https://doi.org/10.1088/1741-2552/ac5c8d [pdf]
  3. Wei Liu, Jie-Lin Qiu, Wei-Long Zheng, Bao-Liang Lu, Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition. IEEE Trans. Cogn. Dev. Syst. 14(2): 715-729 (2022) [pdf]
  4. Xun Wu, Wei-Long Zheng, Ziyi Li, Bao-Liang Lu, Investigating EEG-based functional connectivity patterns for multimodal emotion recognition, Journal of Neural Engineering, vol. 19 (2022) 016012, https://doi.org/10.1088/1741-2552/ac49a7 [pdf]
  5. Dongrui Wu, Yifan Xu, Bao-Liang Lu, Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016. IEEE Trans. Cogn. Dev. Syst. 14(1): 4-19 (2022) [pdf]
  6. Xing Li, Fangyao Shen, Yong Peng, Wanzeng Kong, Bao-Liang Lu, Efficient Sample and Feature Importance Mining in Semi-Supervised EEG Emotion Recognition. IEEE Trans. Circuits Syst. II Express Briefs 69(7): 3349-3353 (2022) [pdf]
  7. Wei Wu, Wei Sun, Q. M. Jonathan Wu, Yimin Yang, Hui Zhang, Wei-Long Zheng, Bao-Liang Lu, Multimodal Vigilance Estimation Using Deep Learning. IEEE Trans. Cybern. 52(5): 3097-3110 (2022) [pdf]
  8. Yong Peng, Yikai Zhang, Wanzeng Kong, Feiping Nie, Bao-Liang Lu, Andrzej Cichocki, S3LRR: A Unified Model for Joint Discriminative Subspace Identification and Semisupervised EEG Emotion Recognition. IEEE Trans. Instrum. Meas. 71: 1-13 (2022) [pdf]
  9. Ziyi Li, Luyu Liu, Yihui Zhu, Bao-Liang Lu, Exploring Sex Differences in Key Frequency Bands and Channel Connections for EEG-based Emotion Recognition, EMBC 2022 [pdf]
  10. Cheng Fei, Rui Li，Le-Dian Liu, Bao-Liang Lu, A Cross-modality Deep Learning Method for Measuring Decision Confidence from Eye Movement Signals: Discrimination of Decision Confidence Levels from EEG Signals，EMBC 2022 [pdf]
  11. Shuai Luo, Yu-Ting Lan, Dan Peng, Ziyi Li, Wei-Long Zheng and Bao-Liang Lu，Multimodal Emotion Recognition in Response to Oil Paintings, EMBC 2022 [pdf]
  12. Yixin Wang, Shuang Qiu, Dan Li, Changde Du, Bao-Liang Lu, Huiguang He, Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition. IEEE CAA J. Autom. Sinica 9(9): 1612-1626 (2022) [pdf]
  13. Shu Jiang, Zuchao Li, Hai Zhao, Bao-Liang Lu, Rui Wang, Tri-training for Dependency Parsing Domain Adaptation. ACM Trans. Asian Low Resour. Lang. Inf. Process. 21(3): 48:1-48:17 (2022) [pdf]
  14. Xing Li, Fangyao Shen, Yong Peng, Wanzeng Kong, Bao-Liang Lu, Efficient Sample and Feature Importance Mining in Semi-Supervised EEG Emotion Recognition. IEEE Trans. Circuits Syst. II Express Briefs 69(7): 3349-3353 (2022) [pdf]
  15. Rui Li, Yiting Wang, Bao-Liang Lu, Measuring Decision Confidence Levels from EEG Using a Spectral-Spatial-Temporal Adaptive Graph Convolutional Neural Network. ICONIP 2022 [pdf]
  16. Tian-Fang Ma, Wei-Long Zheng, Bao-Liang Lu, Few-shot Class-incremental Learning for EEG-based Emotion Recognition. ICONIP 2022 [pdf]
  17. Fangyao Shen, Yong Peng, Guojun Dai, Bao-Liang Lu, Wanzeng Kong: Coupled Projection Transfer Metric Learning for Cross-Session Emotion Recognition from EEG. Syst. 10(2): 47 (2022) [pdf]

2021

  1. Shu Jiang, Zhuosheng Zhang, Hai Zhao, Jiangtong Li, Yang Yang, Bao-Liang Lu, Ning Xia: When SMILES Smiles, Practicality Judgment and Yield Prediction of Chemical Reaction via Deep Chemical Language Processing. IEEE Access 9: 85071-85083 (2021) [pdf]
  2. Wei Wu, Q. M. Jonathan Wu, Wei Sun, Yimin Yang, Xiaofang Yuan, Wei-Long Zheng, Bao-Liang Lu: A Regression Method With Subnetwork Neurons for Vigilance Estimation Using EOG and EEG. IEEE Trans. Cogn. Dev. Syst. 13(1): 209-222 (2021) [pdf]
  3. Wei Wu, Wei Sun, Q. M. Jonathan Wu, Cheng Zhang, Yimin Yang, Hongshan Yu, Bao-Liang Lu: Faster Single Model Vigilance Detection Based on Deep Learning. IEEE Trans. Cogn. Dev. Syst. 13(3): 621-630 (2021) [pdf]
  4. Yong Peng, Wanzeng Kong, Feiwei Qin, Feiping Nie, Jinglong Fang, Bao-Liang Lu, Andrzej Cichocki: Self-Weighted Semi-Supervised Classification for Joint EEG-Based Emotion Recognition and Affective Activation Patterns Mining. IEEE Trans. Instrum. Meas. 70: 1-11 (2021) [pdf]
  5. Li-Ming Zhao; Xu Yan; Bao-Liang Lu: Plug-and-play domain adaptation for cross-subject EEG-based emotion recognition, 2021 AAAI Conference on Artificial Intelligence, Online conference, 2021-02-02 [pdf]
  6. Zhi-Wei Zhao; Wei Liu; Bao-Liang Lu: Multimodal Emotion Recognition Using a Modified Dense Co-Attention Symmetric Network, 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), Online conference, 2021-5-4 to 2021-5-6 [pdf]
  7. Xu Yan*; Li-Ming Zhao*; Bao-Liang Lu: Simplifying Multimodal Emotion Recognition with Single Eye Movement Modality, Proceedings of the 29th ACM International Conference on Multimedia, Online conference, 2021-10-20.(*contributed equally as joint first authors) [pdf]
  8. Rui Li; Yiting Wang; Bao-Liang Lu: A Multi-Domain Adaptive Graph Convolutional Network for EEG-based Emotion Recognition, Proceedings of the 29th ACM International Conference on Multimedia, Online conference, 2021-10-20 [pdf]
  9. Rui-Xiao Ma; Xu Yan; Yu-Zhong Liu; Hua-Liang Li; Bao-Liang Lu: Sex Difference in Emotion Recognition under Sleep Deprivation: Evidence from EEG and Eye-tracking, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Online conference, 2021-10-31 [pdf]
  10. Jian-Ming Zhang; Xu Yan; Zi-Yi Li; Li-Ming Zhao; Yu-Zhong Liu; Hua-Liang Li; Bao-Liang Lu: A Cross-subject and Cross-modal Model for Multimodal Emotion Recognition, The 28th International Conference on Neural Information Processing (ICONIP2021), Online conference, 2021-12-8 [pdf]
  11. Yun Luo; Bao-Liang Lu: Wasserstein-distance-based multi-source adversarial domain adaptation for emotion recognition and vigilance estimation, 2021 International Conference on Bioinformatics and Biomedicine, Online conference, 2021-12-09 [pdf]
  12. Wei-Bang Jiang; Li-Ming Zhao; Ping Guo; Bao-Liang Lu: Discriminating Surprise and Anger from EEG and Eye Movements with a Graph Network, 2021 International Conference on Bioinformatics and Biomedicine, Online conference, 2021-12-09 [pdf]
  13. Yiting Wang; Wei-Bang Jiang; Rui Li; Bao-Liang Lu: Emotion Transformer Fusion: Complementary Representation Properties of EEG and Eye Movements on Recognizing Anger and Surprise, 2021 International Conference on Bioinformatics and Biomedicine, Online conference, 2021-12-09 [pdf]
  14. Rui Li, Le-dian Liu, Bao-Liang Lu: Measuring Human Decision Confidence from EEG Signals in an Object Detection Task, Proc. of 10th International IEEE EMBS Conference on Neural Engineering (NER2021), 2021 [pdf]
  15. Rui Li, Le-dian Liu, Bao-Liang Lu: Discrimination Ability of EEG for Confidence Level in the Visual Perceptual Decision Task, Proc. of 10th International IEEE EMBS Conference on Neural Engineering (NER2021), 2021 [pdf]
  16. Shu Jiang, Rui Wang, Zuchao Li, Masao Utiyama, Kehai Chen, Eiichiro Sumita, Hai Zhao, Bao-Liang Lu: Document-Level Neural Machine Translation with Associated Memory Network. IEICE Trans. Inf. Syst. 104-D(10): 1712-1723 (2021) [pdf]
  17. Hao-Yi Hu, Li-Ming Zhao, Yu-Zhong Liu, Hua-Liang Li, Bao-Liang Lu: A Novel Experiment Setting for Cross-subject Emotion Recognition. EMBC 2021: 6416-6419 [pdf]
  18. Le-Dian Liu, Rui Li, Yu-Zhong Liu, Hua-Liang Li, Bao-Liang Lu: EEG-Based Human Decision Confidence Measurement Using Graph Neural Networks. ICONIP (6) 2021: 291-298 [pdf]

2020

  1. Wei-Long Zheng, Kunpeng Gao, Gang Li, Wei Liu, Chao Liu, Jing-Quan Liu, Guoxing Wang, Bao-Liang Lu: Vigilance Estimation Using a Wearable EOG Device in Real Driving Environment. IEEE Trans. Intelligent Transportation Systems 21(1): 170-184 (2020) [pdf]
  2. Yun Luo, Li-Zhen Zhu, Zi-Yu Wan, Bao-Liang Lu: Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models. Journal of Neural Engineering, vol. 17, no. 5, 056021 (2020) [pdf]
  3. Yingying Jiao, Yini Deng, Yun Luo, Bao-Liang Lu: Driver sleepiness detection from EEG and EOG signals using GAN and LSTM networks. Neurocomputing 408: 100-111 (2020) [pdf]
  4. Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-Liang Lu, Hao Su: Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs. NeurIPS 2020 [pdf]
  5. Yu-Ting Lan, Wei Liu, Bao-Liang Lu, Multimodal Emotion Recognition Using Deep Generalized Canonical Correlation Analysis with an Attention Mechanism. IJCNN, 2020 [pdf]
  6. Wenrui Mu, Bao-Liang Lu, Examining Four Experimental Paradigms for EEG-Based Sleep Quality Evaluation with Domain Adaptation. EMBC 2020: 5913-5916 [pdf]
  7. Shu Jiang, Hai Zhao, Zuchao Li, Bao-Liang Lu: Document-level Neural Machine Translation with Document Embeddings. CoRR abs/2009.08775 (2020) [pdf]
  8. Dongrui Wu, Yifan Xu, Bao-Liang Lu: Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progresses Since 2016. CoRR abs/2004.06286 (2020) [pdf]
  9. Xun Wu, Wei-Long Zheng, Bao-Liang Lu: Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition. CoRR abs/2004.01973 (2020) [pdf]
  10. Le-Yan Tao, Bao-Liang Lu, Recognition under Sleep Deprivation Using a Multimodal Residual LSTM Network. IJCNN 2020: 1-8 [pdf]
  11. Yong Peng, Qingxi Li, Wanzeng Kong, Jianhai Zhang, Bao-Liang Lu, Andrzej Cichocki: Joint Semi-Supervised Feature Auto-Weighting and Classification Model for EEG-Based Cross-Subject Sleep Quality Evaluation. ICASSP 2020: 946-950 [pdf]
  12. Chen-Li Yao, Bao-Liang Lu: A Robust Approach to Estimating Vigilance from EEG with Neural Processes. BIBM 2020: 1202-1205 [pdf]
  13. Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-Liang Lu, Hao Su: Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks. CoRR abs/2010.13547 (2020)

2019

  1. Wei-Long Zheng, Jia-Yi Zhu, Bao-Liang Lu: Identifying Stable Patterns over Time for Emotion Recognition from EEG. IEEE Trans. Affective Computing, 10(3): 417-429 (2019) [pdf]
  2. Lu Gan, Wei Liu, Yun Luo, Xun Wu, Bao-Liang Lu, A Cross-Culture Study on Multimodal Emotion Recognition Using Deep Learning. In: Gedeon T., Wong K., Lee M. (eds) Neural Information Processing. ICONIP 2019: 670-680 [pdf]
  3. Shu Jiang, Rui Wang, Zuchao Li, Masao Utiyama, Kehai Chen, Eiichiro Sumita, Hai Zhao, Bao-Liang Lu: Document-level Neural Machine Translation with Inter-Sentence Attention. CoRR abs/1910.14528 (2019) [pdf]
  4. Wei Liu, Jie-Lin Qiu, Wei-Long Zheng, Bao-Liang Lu: Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis. CoRR abs/1908.05349 (2019) [pdf]
  5. Bo-Qun Ma, He Li, Wei-Long Zheng, Bao-Liang Lu: Reducing the Subject Variability of EEG Signals with Adversarial Domain Generalization. ICONIP (1) 2019: 30-42 [pdf]
  6. Huangfei Jiang, Xiya Guan, Wei-Ye Zhao, Li-Ming Zhao, Bao-Liang Lu: Generating Multimodal Features for Emotion Classification from Eye Movement Signals. Aust. J. Intell. Inf. Process. Syst. 15(3): 59-66 (2019) [pdf]
  7. Wei-Long Zheng, Wei Liu, Yifei Lu, Bao-Liang Lu, and Andrzej Cichocki, EmotionMeter: A Multimodal Framework for Recognizing Human Emotions. IEEE Transactions on Cybernetics, 49(3):1110-1122, 2019 [pdf]
  8. Yun Luo, Li-Zhen Zhu, Bao-Liang Lu, A GAN-Based Data Augmentation Method for Multimodal Emotion Recognition. ISNN (1) 2019: 141-150 [pdf]
  9. Xun Wu, Wei-Long Zheng, Bao-Liang Lu, Identifying Functional Brain Connectivity Patterns for EEG-Based Emotion Recognition. IEEE NER 2019: 235-238 [pdf]
  10. Tian-Hao Li, Wei Liu, Wei-Long Zheng, Bao-Liang Lu, Classification of Five Emotions from EEG and Eye Movement Signals: Discrimination Ability and Stability over Time. IEEE NER 2019: 607-610 [pdf]
  11. Li-Ming Zhao, Rui Li, Wei-Long Zheng, Bao-Liang Lu, Classification of Five Emotions from EEG and Eye Movement Signals: Complementary Representation Properties. IEEE NER 2019: 611-614 [pdf]
  12. Bo-Qun Ma, He Li, Yun Luo, Bao-Liang Lu, Depersonalized Cross-Subject Vigilance Estimation with Adversarial Domain Generalization, Proc. IJCNN 2019, Budabest [pdf]
  13. Lan-Qing Bao, Jie-Lin Qiu, Hao Tang, Wei-Long Zheng, Bao-Liang Lu, Investigating Sex Differences in Classification of Five Emotions from EEG and Eye Movement Signals, Proc. IEEE EMBC 2019, Berlin [pdf]
  14. Jiang-Jian Guo, Rong Zhou, Li-Ming Zhao, Bao-Liang Lu, Multimodal Emotion Recognition from Eye Image, Eye Movement and EEG Using Deep Neural Networks, Proc. IEEE EMBC 2019, Berlin [pdf]
  15. Jiaxin Ma, Hao Tang, Wei-Long Zheng, Bao-Liang Lu, Emotion Recognition using Multimodal Residual LSTM Network, Proc. ACM Multimedia 2019. [pdf]
  16. Shu Jiang, Zhuosheng Zhang, Hai Zhao, Jiangtong Li, Yang Yang, Bao-Liang Lu, Ning Xia, Judging Chemical Reaction Practicality From Positive Sample only Learning. CoRR abs/1904.09824, 2019 [pdf]

2018

  1. Rui Wang, Hai Zhao, Sabine Ploux, Bao-Liang Lu, Masao Utiyama, Eiichiro Sumita, Graph-based Bilingual Word Embedding for Statistical Machine Translation, ACM Transaciton on Asian and Low-Resource Language Information Processing, Vol.17(4), 2018 [pdf]
  2. He Li, Wei-Long Zheng, Bao-Liang Lu, Multimodal Vigilance Estimation with Adversarial Domain Adaptation Networks, in Proc. IEEE IJCNN2018 [pdf]
  3. Jia-Jun Tong, Yun Luo, Bo-Qun Ma, Wei-Long Zheng, Bao-Liang Lu, Xiao-Qi Song, Shi-Wei Ma, Sleep Quality Estimation with Adversarial Domain Adaptation: From Laboratory to Real Scenario, Proc. IEEE IJCNN2018 [pdf]
  4. Yun Luo, Bao-Liang Lu, EEG Data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN, Proc. IEEE EMBC2018 [pdf]
  5. Yimin Yang, Q. M. Jonathan Wu, Wei-Long Zheng, Bao-Liang Lu: EEG-Based Emotion Recognition Using Hierarchical Network With Subnetwork Nodes. IEEE Trans. Cognitive and Developmental Systems 10(2): 408-419 (2018) [pdf]
  6. Jie-Lin Qiu, Wei Liu, Bao-Liang Lu: Multi-view Emotion Recognition Using Deep Canonical Correlation Analysis. ICONIP (5) 2018: 221-231 [pdf]
  7. Yun Luo, Si-Yang Zhang, Wei-Long Zheng, Bao-Liang Lu: WGAN Domain Adaptation for EEG-Based Emotion Recognition. ICONIP (5) 2018: 275-286 [pdf]
  8. Li-Ming Zhao, Xin-Wei Li, Wei-Long Zheng, Bao-Liang Lu: Active Feedback Framework with Scan-Path Clustering for Deep Affective Models. ICONIP (2) 2018: 330-340 [pdf]
  9. He Li, Yi-Ming Jin, Wei-Long Zheng, Bao-Liang Lu: Cross-Subject Emotion Recognition Using Deep Adaptation Networks. ICONIP (5) 2018: 403-413 [pdf]
  10. Yini Deng, Yingying Jiao, Bao-Liang Lu: Driver Sleepiness Detection Using LSTM Neural Network. ICONIP (4) 2018: 622-633 [pdf]
  11. Changde Du, Changying Du, Hao Wang, Jinpeng Li, Wei-Long Zheng, Bao-Liang Lu, Huiguang He: Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data. ACM Multimedia 2018: 108-116 [pdf]
  12. Changde Du, Changying Du, Hao Wang, Jinpeng Li, Wei-Long Zheng, Bao-Liang Lu, Huiguang He: Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data. CoRR abs/1808.02096 (2018) [pdf]

2017

  1. Yimin Yang, Q. M. Jonathan Wu, Wei-Long Zheng, and Bao-Liang Lu, EEG-based emotion recognition using hierarchical network with subnetwork nodes, IEEE Transactions on Cognitive and Developmental Systems, DOI: 10.1109/TCDS.2017.2685338 [pdf]
  2. Wei-Long Zheng and Bao-Liang Lu, A multimodal approach to estimating vigilance using EEG and forehead EOG. Journal of Neural Engineering, 14(2): 026017, 2017 [pdf]
  3. Yong Peng, Bao-Liang Lu: Discriminative extreme learning machine with supervised sparsity preserving for image Classification, Neurocomputing, 261: 242-252, 2017 [pdf]
  4. Yong Peng, Bao-Liang Lu: Robust structured sparse representation via half-quadratic optimization for face recognition. Multimedia Tools Appl. 76(6): 8859-8880, 2017 [pdf]
  5. Kai-Ming Jiang, Ya-Jing Chen, Jin-Xiong Lv, Bao-Liang Lu, Lei Xu: Bootstrapping integrative hypothesis test for identifying biomarkers that differentiates lung cancer and chronic obstructive pulmonary disease. Neurocomputing, 269: 40-46, 2017 [pdf]
  6. Si-Yuan Wu, Moritz Schaefer, Wei-Long Zheng, and Bao-Liang Lu, Neural Patterns between Chinese and Germans for EEG-based Emotion Recognition, Proc. of the 8th International IEEE EMBS Conference on Neural Engineering, Shanghai, 2017 [pdf]
  7. Zhen-Feng Shi, Chang Zhou, Wei-Long Zheng and Bao-Liang Lu, Attention Evaluation with Eye Tracking Glasses for EEG-based Emotion Recognition, Proc. of the 8th International IEEE EMBS Conference on Neural Engineering, Shanghai, 2017 [pdf]
  8. Li-Huan Du, Wei Liu, Wei-Long Zheng and Bao-Liang Lu, Detecting Driving Fatigue with Multimodal Deep Learning, Proc. of the 8th International IEEE EMBS Conference on Neural Engineering, Shanghai, 2017 [pdf]
  9. Yingying Jiao and Bao-Liang Lu, An Alpha Wave Pattern from Attenuation to Disappearance for Predicting the Entry into Sleep during Simulated Driving, Proc. of the 8th International IEEE EMBS Conference on Neural Engineering, Shanghai, 2017 [pdf]
  10. Hao Tang, Wei Liu, Wei-Long Zheng, and Bao-Liang Lu, Multimodal Emotion Recognition Using Deep Neural Networks, Proc. ICONIP2017, Guangzhou, 2017 [pdf]
  11. Xue Yan, Wei-Long Zheng, Wei Liu, and Bao-Liang Lu, Identifying Gender Differences in Multimodal Emotion Recognition Using Bimodal Deep AutoEncoder, Proc. ICONIP2017, Guangzhou, 2017 [pdf]
  12. Xue Yan, Wei-Long Zheng, Wei Liu, and Bao-Liang Lu, Investigating Gender Differences of Brain Areas in Emotion Recognition Using LSTM Neural Network, Proc. ICONIP2017, Guangzhou, 2017 [pdf]
  13. Xing-Zan Zhang, Wei-Long Zheng, and Bao-Liang Lu, EEG-Based Sleep Quality Evaluation with Deep Transfer Learning, Proc. ICONIP2017, Guangzhou, 2017 [pdf]
  14. Yi-Ming Jin, Yu-Dong Luo, Wei-Long Zheng, Bao-Liang Lu, EEG-based Emotion Recognition Using Domain Adaptation Network, Proc. ICOT2017, Singapore, 2017 [pdf]
  15. Yingying Jiao and Bao-Liang Lu, Detecting Driver Sleepiness from EEG Alpha Wave during Daytime Driving, Proc. IEEE BIBM2017, Kansas City, USA, 2017 [pdf]
  16. Changde Du, Changying Du, Jinpeng Li, Wei-Long Zheng, Bao-Liang Lu, Huiguang He: Semi-supervised Bayesian Deep Multi-modal Emotion Recognition. CoRR abs/1704.07548 (2017)

2016

  1. Wei-Long Zheng and Bao-Liang Lu*, Personalizing EEG-based Affective Models with Transfer Learning, Proc. of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16), New York [pdf]
  2. Rui Wang, Hai Zhao*, Sabine Ploux*, Bao-Liang Lu and Masao Utiyama, A Bilingual Graph-based Semantic Model for Statistical Machine Translation, Proc. of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16), New York [pdf]
  3. Xue-Qin Huo, Wei-Long Zheng, Bao-Liang Lu*, Driving Fatigue Detection with Fusion of EEG and Forehead EOG, Proc. of 2016 International Joint Conference on Neural Networks (IJCNN-16), Vancouver [pdf]
  4. Li-Li Wang, Wei-Long Zheng, Hai-Wei Ma and Bao-Liang Lu*, Measuring Sleep Quality from EEG with Machine Learning Approaches, Proc. of 2016 International Joint Conference on Neural Networks (IJCNN-16) , Vancouver [pdf]
  5. Jincheng Mei, Hao Zhang, Bao-Liang Lu*, On the Reducibility of Submodular Functions, Proc. of The 19th International Conference on Artificial Intelligence and Statistics, May 9 - 11, 2016, Cadiz, Spain [pdf]
  6. Wei-Long Zheng, Shan-Chun Shen and Bao-Liang Lu*, Online Depth Image-Based Object Tracking with Sparse Representation and Object Detection, Neural Process Letter [pdf]
  7. Yong Peng, Wei-Long Zheng, Bao-Liang Lu*, An Unsupervised Discriminative Extreme Learning Machine and its Applications to Data Clustering. Neurocomputing 174: 250-264 (2016) [pdf]
  8. Yong Peng, Bao-Liang Lu*, Discriminative Manifold Extreme Learning Machine and Applications to Image and EEG Signal Classification. Neurocomputing 174: 265-277 (2016) [pdf]
  9. Wei-Long Zheng, Jia-Yi Zhu, Bao-Liang Lu*, Identifying Stable Patterns over Time for Emotion Recognition from EEG. CoRR abs/1601.02197 (2016) [pdf]
  10. Wei Liu, Wei-Long Zheng, Bao-Liang Lu*, Multimodal Emotion Recognition Using Multimodal Deep Learning. CoRR abs/1602.08225 (2016) [pdf]
  11. Yingying Jiao, Bao-Liang Lu*, Detecting Slow Eye Movement for Recognizing Driver's Sleep Onset Period with EEG features, Proc. of IEEE EMBC2016, Florida [pdf]
  12. Wei Liu, Wei-Long Zheng, Bao-Liang Lu*, Emotion Recognition using Multimodal Deep Learning, Proc. of ICONIP2016, Kyoto, 2016 [pdf]
  13. Nan Zhang, Wei-Long Zheng, Wei Liu, Bao-Liang Lu*, Continuous Vigilance Estimation using LSTM Neural Networks, Proc. of ICONIP2016, Kyoto, 2016 [pdf]
  14. Rui Wang, Masao Utiyama, Isao Goto, Eiichiro Sumita, Hai Zhao, Bao-Liang Lu: Converting Continuous-Space Language Models into N-gram Language Models with Efficient Bilingual Pruning for Statistical Machine Translation. ACM Trans. Asian & Low-Resource Lang. Inf. Process. 15(3):11:1-11:26 [pdf]
  15. Rui Wang, Hai Zhao, Bao-Liang Lu, Masao Utiyama, Eiichiro Sumita: Connecting Phrase based Statistical Machine Translation Adaptation. Proc. of International Conference on Computational Linguistics 2016, Osaka (COLING 2016): 3135-3145 [pdf]
  16. Rui Wang, Hai Zhao, Sabine Ploux, Bao-Liang Lu, Masao Utiyama: A Novel Bilingual Word Embedding Method for Lexical Translation Using Bilingual Sense Clique. CoRR abs/1607.08692 (2016) [pdf]
  17. Jincheng Mei, Hao Zhang, Bao-Liang Lu: On the Reducibility of Submodular Functions. CoRR abs/1601.00393 (2016)
  18. Rui Wang, Hai Zhao, Bao-Liang Lu, Masao Utiyama, Eiichiro Sumita: Connecting Phrase based Statistical Machine Translation Adaptation. CoRR abs/1607.08693 (2016)

2015

  1. Mu Li, Wei Bi, James T. Kwok, and Bao-Liang Lu, Large-Scale Nystrom Kernel Matrix Approximation Using Randomized SVD. IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 1, pp. 152-164, 2015. [pdf]
  2. Yong Peng, Bao-Liang Lu, and Suhang Wang, Enhanced Low-rank Representation via Sparse Manifold Adaption for Semi-supervised Learning, Neural Networks, vol. 65, pp. 1-17, 2015. [pdf]
  3. Yong Peng and Bao-Liang Lu, Hybrid Learning Clonal Selection Algorithm, Information Sciences, vol. 296, pp. 128-146, 2015. [pdf]
  4. Yong Peng, Suhang Wang, Xianzhong Long, and Bao-Liang Lu, Discriminative Graph Regularized Extreme Learning Machine and Its Application to Face Recognition, Neurocomputing, vol. 149, pp. 340-353, 2015. [pdf]
  5. Rui Wang, Masao Utiyama, Isao Goto, Eiichiro Sumita, Hai Zhao, Bao-Liang Lu, Converting Continuous-Space Language Models into N-gram Language Models with Efficient Bilingual Pruning for Statistical Machine Translation. ACM Transactions on Asian Low-Resourse Languange Information Process. [pdf]
  6. Jincheng Mei, Kang Zhao, Bao-Liang Lu, On Unconstrained Quasi-Submodular Function Optimization, The 29th AAAI Conference on Artificial Intelligence, Texas (AAAI2015):1191-1197. [pdf]
  7. Yangcheng He, Hongtao Lu, Bao-Liang Lu, Graph regularized non-negative local coordinate factorization with pairwise constraints for image representation. Proc. of the 2015 IEEE International Conference on Multimedia and Expo, Colorado (ICME 2015). [pdf]
  8. Li Wu, Kang Zhao, Hongtao Lu, Zhen Wei, Bao-Liang Lu, Distance Preserving Marginal Hashing for image retrieval. Proc. of the 2015 IEEE International Conference on Multimedia and Expo, Colorado (ICME 2015). [pdf]
  9. Wei-Long Zheng, Hao-Tian Guo, and Bao-Liang Lu, Revealing Critical Channels and Frequency Bands for EEG-based Emotion Recognition with Deep Belief Network, Proc. of the 7th International IEEE EMBS Conference on Neural Engineering, Montpellier (IEEE EMBS2015). [pdf]
  10. Xiang-Yu Gao, Yu-Fei Zhang, Wei-Long Zheng, and Bao-Liang Lu, Evaluating Driving Fatigue Detection Algorithms Using Eye Tracking Glasses, Proc. of the 7th International IEEE EMBS Conference on Neural Engineering, Montpellier (IEEE EMBS2015). [pdf]
  11. Yu-Fei Zhang, Xiang-Yu Gao, Jia-Yi Zhu, Wei-Long Zheng, and Bao-Liang Lu, A Novel Approach to Driving Fatigue Detection Using Forehead EOG, Proc. of the 7th International IEEE EMBS Conference on Neural Engineering, Montpellier (IEEE EMBS2015). [pdf]
  12. Yong Peng and Bao-Liang Lu, Robust Group Sparse Representation via Half-quadratic Optimization for Face Recognition, Proc. of Canadian Conference on Electrical and Computer Engineering, Halifax (CCECE'15). [pdf]
  13. Wei-Long Zheng, Roberto Santana, and Bao-Liang Lu, Comparision of classification methods for EEG-based emotion recognition, Proc. of the 2015 World Congress on Medical Physics and Biomedical Engineering, Toronto. [pdf]
  14. Jia-Yi Zhu, Wei-Long Zheng, and Bao-Liang Lu, Cross-subject and Cross-gender Emotion Classification from EEG, Proc. of the 2015 World Congress on Medical Physics and Biomedical Engineering, Toronto. [pdf]
  15. Rui Wang, Hai Zhao, Bao-Liang Lu, Masao Utiyama and Eiichiro Sumita, Bilingual Continuous-Space Language Model Growing for Statistical Machine Translation, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 7, pp. 1209-1220. [pdf]
  16. Wei-Long Zheng, Bao-Liang Lu, Investigating Critical Frequency Bands and Channels for EEG-based Emotion Recognition with Deep Neural Networks, IEEE Transactions on Autonomous Mental Development. [pdf]
  17. Yi-Fei Lu, Wei-Long Zheng, Bin-Bin Li, Bao-Liang Lu, Combining Eye Movements and EEG to Enhance Emotion Recognition, Proc. of the International Joint Conference on Artificial Intelligence, Buenos Aires (IJCAI2015). [pdf]
  18. Rui Wang , Hai Zhao and Bao-Liang Lu, English to Chinese Translation: How Chinese Character Matters?. Proc. of the 29th Pacific Asia Conference on Language, Information and Computation, Shanghai. [pdf]
  19. Yang Cao and Bao-Liang Lu, Intensity-Depth Face Alignment Using Cascade Shape Regression, Proc. of the 22nd International Conference on Neural Information Processing, Istanbul (ICONIP2015). [pdf]
  20. Yong-Qi Zhang, Wei-Long Zheng, Jia-Yi Zhu, Bao-Liang Lu, Transfer Components Between Subjects for EGG-based Driving Fatigue Detection, Proc. of the 22nd International Conference on Neural Information Processing, Istanbul (ICONIP2015). [pdf]
  21. Wei-Long Zheng, Yong-Qi Zhang, Jia-Yi Zhu, Bao-Liang Lu, Transfer Components between Subjects for EEG-based Emotion Recognition, Proc. of the 6th International Conference on Affective Computing and Intelligent Interaction, Xi’an (ACII2015). [pdf]

2014

  1. Xiaolin Wang, Hai Zhao and Bao-Liang Lu, A Meta-Top-Down Method for Large-Scale Hierarchical Classification, IEEE Transactions on Knowledge and Data Engineering, vol. 26, pp. 500-513, 2014. [pdf]
  2. Xiao-Wei Wang, Dan Nie, Bao-Liang Lu, Emotional State Classification from EEG Data Using Machine Learning Approach, Neurocomputing, vol. 129, pp. 94-106, 2014. [pdf]
  3. Xiaolin Wang, Yangyang Chen, Hai Zhao Bao-Liang Lu, Parallelized Extreme Learning Machine Ensemble Based on Min-max Modular Network, Neurocomputing, vol. 128, pp. 31-41, 2014. [pdf]
  4. Wei-Long Zheng, Jia-Yi Zhu, Yong Peng, Bao-Liang Lu, EEG-Based Emotion Classification Using Deep Belief Networks, IEEE International Conference on Multimedia and Expo, Bandung (IEEE ICME2014). [pdf]
  5. Wei-Long Zheng, Bo-Nan Dong and Bao-Liang Lu, Multimodal Emotion Recognition Using EEG and Eye Tracking Data, International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago (IEEE EMBC2014). [pdf]
  6. Yong Peng, Jia-Yi Zhu, Wei-Long Zheng, Bao-Liang Lu, EEG-Based Emotion Recognition with Manifold Regularized Extreme Learning Machine, International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago (IEEE EMBC2014). [pdf]
  7. Xuemin Zhu, Wei-Long Zheng, Bao-Liang Lu, Xiaoping Chen, Shanguang Chen and Chunhui Wang, EOG-Based Drowsiness Detection using Convolutional Neural Networks, International Joint Conference on Neural Networks, Beijing (IJCNN). 2014: 128-134. [pdf]
  8. Jia-Yi Zhu, Wei-Long Zheng, Ruo-Nan Duan, Yong Peng and Bao-Liang Lu, EEG-Based Emotion Recognition Using Discriminative Graph Regularized Extreme Learning Machine, International Joint Conference on Neural Networks, Beijing (IJCNN). 2014: 525-532. [pdf]
  9. Ying-Ying Jiao, Yong Peng and Bao-Liang Lu, Recognizing Slow Eye Movement for Driver Fatigue Detection with Machine Learning Approach, International Joint Conference on Neural Networks, Beijing (IJCNN). 2014: 4035-4041. [pdf]
  10. Wei-Long Zheng, Jia-Yi Zhu and Bao-Liang Lu, Multimodal Emotion Analysis in Response to Multimedia, Proc. of IEEE International Conference on Multimedia and Expo, Bandung (IEEE ICME2014). [pdf]
  11. Rui Wang, Hai Zhao, and Bao-Liang Lu, Masao Utiyama and Eiichro Sumita, Neural Network Based Bilingual Language Model Growing for Statistical Machine Translation, Proc. of the 2014 Conference on Empirical Methods on Natural Language Processing, Doha (EMNLP2014). [pdf]
  12. Yong Peng, Bao-Liang Lu, Discriminative Manifold Extreme Learning Machine and Applications to Image and EEG Signal Classification, International Conference on Extreme Learning Machines, Singapore (ELM2014). [pdf]
  13. Yong Peng, Bao-Liang Lu, An Unsupervised Discriminative Extreme Learning Machine, International Conference on Extreme Learning Machines (ELM2014). [pdf]
  14. Shan-Chun Shen, Wei-Long Zheng and Bao-Liang Lu, Online Object Tracking Based on Depth Image with Sparse Coding, Proc. of the 21st International Conference of Neural Information Processing, Kuching (ICONIP2014). [pdf]
  15. Jincheng Mei and Bao-Liang Lu, Saliency Level Set Evolution, Proc. of the 21st International Conference of Neural Information Processing, Kuching (ICONIP2014).4035-4041. [pdf]
  16. Jing-Nan Gu, Hong-Tao Lu, Bao-Liang Lu, An Integrated Gaussian Mixture Model to Estimate Vigilance Level Based on EEG Recordings, Neurocomputing vol. 129, pp. 107-113, 2014. [pdf]
  17. Jincheng Mei, Kang Zhao, Bao-Liang Lu: Unconstrained Quasi-Submodular Function Optimization. CoRR abs/1408.4389 (2014)

2013

  1. Li-Chen Shi and Bao-Liang Lu, EEG-based vigilance estimation using extreme learning machines. Neurocomputing, vol. 102, pp. 135-143, 2013. [pdf]
  2. Hai Zhao, Masao Utiyama, Eiichiro Sumita, Bao-Liang Lu, An Empirical Study on Word Segmentation for Chinese Machine Translation. Proc. of 14th International Conference on Intelligent Text Processing and Computational Linguistics, Samos (CICLing2013), pp. 248-263, 2013. [pdf]
  3. Yong Peng and Bao-Liang Lu, A Hierarchical Particle Swarm Optimizer with Latin Sampling Based Memetic Algorithm for Numerical Optimization, Applied Soft Computing, vol. 13, no. 5, pp. 2823-2836, 2013. [pdf]
  4. Yong Peng, Suhang Wang, Xianzhong Long and Bao-Liang Lu, Discriminative Graph Regularized Extreme Learning Machine and Its application to Face Recognition, Neurocomputing: 340-353. [pdf]
  5. Li-Chen Shi, Ruo-Nan Duan and Bao-Liang Lu, A Robust Principal Component Analysis Algorithm for EEG-Based Vigilance Estimation, International Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, (EMBS 2013): 6623-6626. [pdf]
  6. Li-Chen Shi, Ying-Ying Jiao and Bao-Liang Lu, Differential Entropy Feature for EEG-based Vigilance Estimation, International Conference of the IEEE Engineering in Medicine and Biology Society, Osaka (EMBS 2013): 6627-6630. [pdf]
  7. Ruo-Nan Duan, Jia-Yi Zhu and Bao-Liang Lu, Differential Entropy Feature for EEG-based Emotion Classification, Proc. of the 6th International IEEE EMBS Conference on Neural Engineering, San Diego (NER 2013): 81-84. [pdf]
  8. Yong Peng, Suhang Wang, Shen Wang and Bao-Liang Lu, Structure Preserving Low-rank Representation for Semi-supervised Face Recognition, Proc. of the 20th International Conference of Neural Information Processing, Deagu (ICONIP 2013): 148-155. [pdf]
  9. Yong Peng, Suhang Wang and Bao-Liang Lu, Marginalized Denoising Autoencoder via Graph Regularization for Domain Adaptation, Proc. of the 20th International Conference of Neural Information Processing, Deagu (ICONIP 2013): 156-163. [pdf]
  10. Yang Cao and Bao-Liang Lu, Real-time Head Detection with Kinect for Driving Fatigue Detection, Proc. of the 20th International Conference of Neural Information Processing, Deagu (ICONIP 2013): 600-607. [pdf]
  11. Fan Li, Xiao-Wei Wang and Bao-Liang Lu, Detection of Driving Fatigue Based on Grip Force on Steering Wheel with Wavelet Transformation and Support Vector Machine, Proc. of the 20th International Conference of Neural Information Processing, Deagu (ICONIP 2013): 141-148. [pdf]
  12. Xiao-Lin Wang, Hai Zhao and Bao-Liang Lu, Labeled Alignment for Recognizing Textual Entailment, Proc. of The 6th International Joint Conference on Natural Language Processing, Nagoya (IJCNLP 2013): 605-613. [pdf]
  13. Rui Wang, Masao Utiyama, Isao Goto, Eiichro Sumita, Hai Zhao and Bao-Liang Lu, Converting Continuous-Space Language Models into N-gram Language Models for Statistical Machine Translation, Proc. of the 2013 Conference on Empirical Methods on Natural Language Processing, Seattle (EMNLP 2013): 845-850. [pdf]

2012

  1. Bing Li, Xiao-Chen Lian, Bao-Liang Lu, Gender classification by combining clothing, hair and facial component classifiers. Neurocomputing 76(1): 18-27 (2012) [pdf]
  2. Tian-Xiang Wu, Xiao-Chen Lian, Bao-Liang Lu, Multi-view gender classification using symmetry of facial images. Neural Computing and Applications 21(4): 661-669 (2012) [pdf]
  3. Ruo-Fei Du, Ren-Jie Liu, Tian-Xiang Wu, Bao-Liang Lu, Online Vigilance Analysis Combining Video and Electrooculography Features. Proc. of the 19th International Conference of Neural Information Processing, Doha (ICONIP 2012): 447-454 [pdf]
  4. Ruo-Nan Duan, Xiao-Wei Wang, Bao-Liang Lu, EEG-Based Emotion Recognition in Listening Music by Using Support Vector Machine and Linear Dynamic System. Proc. of the 19th International Conference of Neural Information Processing, Doha (ICONIP 2012): 468-475 [pdf]
  5. Hui Sun, Bao-Liang Lu, EEG-Based Fatigue Classification by Using Parallel Hidden Markov Model and Pattern Classifier Combination. Proc. of the 19th International Conference of Neural Information Processing, Doha (ICONIP 2012): 484-491 [pdf]
  6. Shaohua Yang, Hai Zhao and Bao-Liang Lu, A Machine Translation Approach for Chinese Whole-Sentence Pinyin-to-Character Conversion, PACLIC-26, Bali, Indonesia, November, 2012 [pdf]
  7. Yangyang Chen, Bao-Liang Lu, Hai Zhao: Parallel learning of large-scale multi-label classification problems with min-max modular LIBLINEAR. International Joint Conference on Neural Networks, Brisbane (IJCNN) 2012: 1-7 [pdf]
  8. Zheng-Ping Wei, Bao-Liang Lu, Online vigilance analysis based on electrooculography. International Joint Conference on Neural Networks, Brisbane (IJCNN 2012): 1-7 [pdf]
  9. Yong Peng, Bao-Liang Lu, Immune clonal algorithm based feature selection for epileptic EEG signal classification. International Conference on Information Sciences, Signal Processing and their Applications, Montreal (ISSPA 2012): 848-853 [pdf]
  10. Shaohua Yang, Hai Zhao, Xiaolin Wang and Bao-liang Lu, Spell Checking for Chinese, Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12), pages 730-736, Istanbul, Turkey, May, 2012 [pdf]
  11.  Chun-Fang Gao, Bao-Liang Lu and Jia-Xin Ma, A New Method of Extracting Vigilant Feature from Electrooculography by Using Wavelet Packet Transform, Chinese Journal of Biomedical Engineering, vol. 31, no. 5, pp. 641-648 (2012) [pdf]
  12. Dan Nie, Xiao-Wei Wang, Ruo-Nan Duan and Bao-Liang Lu, A Survey on EEG based Emotion Recognition, Chinese Journal of Biomedical Engineering, vol. 31, no. 4, pp. 595-606 (2012) [pdf]

2011

  1. Bing Li, Rong Xiao, Zhiwei Li, Rui Cai, Bao-Liang Lu, and Lei Zhang, Rank-SIFT: Learning to Rank Repeatable Local Interest Points, Proc. of 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, USA, June 20-25, 2011. [pdf]
  2. Mu Li, Xiao-Chen Lian, James T. Kwok, and Bao-Liang Lu, Time and Space Efficient Spectral Clustering via Column Sampling, Proc. of 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, USA, June 20-25, 2011. [pdf]
  3. Dan Nie, Xiao-Wei Wang, Li-Chen Shi, and Bao-Liang Lu, EEG-based Emotion Recognition during Watching Movies, Proc. of 5th International IEEE EMBC Conference on Neural Engineering (NER), pp. 667-670, Cancun, Mexico, April 27-May 1, 2011. [pdf]
  4. Wen-Yun Yang , Bao-Liang Lu, James T. Kwok, Incorporating Cellular Sorting Structure for Better Prediction of Protein Subcellular Locations. Journal of Experimental and Theoretical Artificial Intelligence (JETAI)，vol. 23, pp. 79-95, 2011. [pdf]
  5. Zheng Ji and Bao-Liang Lu: "A Support Vector Machine Classifier with Automatic Confidence and Its Application to Gender Classification", Neuraocomputing, vol. 74, pp. 1926-1935, 2011. [pdf]
  6. Bao-Liang Lu, Xiao-Lin Wang, Yang Yang, and Hai Zhao: "Learning from Imbalanced Data Sets with a Min-max Modular Support Vector Machine ", Frontiers of Electrical and Electronic Engineering in China, vol. 6, no. 1, pp. 56-71, 2011. [pdf]
  7. L. C. Shi, Y. Li, R. H. Sun,B. L. Lu, "A sparse common spatial pattern algorithm for brain-computer interface", 18th International Conference on Neural Information Processing, Shanghai (ICONIP 2011). [pdf]
  8. X. W. Wang, D. Nie, B. L. Lu, "EEG-based emotion recognition using frequency domain features and support vector machines", 18th International Conference on Neural Information Processing, Shanghai (ICONIP 2011). [pdf]
  9. J. Wu, L. C. Shi, B. L. Lu, "Removing unrelated features based on linear dynamical system for motor-imagery-based brain-computer interface", 18th International Conference on Neural Information Processing, Shanghai (ICONIP 2011). [pdf]
  10. Zhong-Lei Gu, Li-Chen Shi, Bao-Liang Lu, "Evidence of Rapid Gender Processing Revealed by ERSP", 33rd Annual International Conference of the IEEE EMBS, Boston. [pdf]
  11. Hao-Yu Cai, Jia-Xin Ma, Li-Chen Shi, Bao-Liang Lu, A Novel Method for EOG Features Extraction from the Forehead, 33rd Annual International Conference of the IEEE EMBS, Boston. [pdf]
  12. Xiao-lin Wang, Hai Zhao, Bao-Liang Lu, Redundancy Removal to Selectively Diversify Information Retrieval Results, The 9th NTCIR Workshop Meeting. Evaluation of Information Access Technologies: Information Retrieval, Tokyo. [pdf]
  13. Xiao-lin Wang, Hai Zhao, Bao-Liang Lu, GeoTime Retrieval through Passage-based Learning to Rank, The 9th NTCIR Workshop Meeting. Evaluation of Information Access Technologies: Information Retrieval, Tokyo. [pdf]
  14. Xiao-lin Wang, Hai Zhao, Bao-Liang Lu, Enhance Top-down method with Meta-Classification for Very Large-scale Hierarchical Classification, The 5th International Joint Conference on Natural Language Processing, Chiang Mai (IJCNLP 2011). [pdf]
  15. Hao-Yu Cai, Jia-Xin Ma, Li-Chen Shi, Bao-Liang Lu : A novel method for EOG features extraction from the forehead. EMBC 2011: 3075-3078 [pdf]
  16. Jian Zhang, Hai Zhao, Liqing Zhang, Bao-Liang Lu: An Empirical Comparative Study on Two Large-Scale Hierarchical Text Classification Approaches. Int. J. Comput. Process. Orient. Lang. 23(4): 309-326 (2011)
  17. Jing-Nan Gu, Hong-Jun Liu, Hong-Tao Lu, Bao-Liang Lu : An Integrated Hierarchical Gaussian Mixture Model to Estimate Vigilance Level Based on EEG Recordings. ICONIP (1) 2011: 380-387 [pdf]
  18. Xiaolin Wang, Hai Zhao, Bao-Liang Lu: GeoTime Retrieval through Passage-based Learning to Rank. NTCIR 2011 [pdf]
  19. Xiaolin Wang, Hai Zhao, Bao-Liang Lu: Redundancy Removal to Selectively Diversify Information Retrieval Results. NTCIR 2011
  20. Bao-Liang Lu, Liqing Zhang, James T. Kwok: Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part I. Lecture Notes in Computer Science 7062, Springer 2011, ISBN 978-3-642-24954-9
  21. Bao-Liang Lu, Liqing Zhang, James T. Kwok: Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II. Lecture Notes in Computer Science 7063, Springer 2011, ISBN 978-3-642-24957-0
  22. Bao-Liang Lu, Liqing Zhang, James T. Kwok: Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part III. Lecture Notes in Computer Science 7064, Springer 2011, ISBN 978-3-642-24964-8

2010

  1. Yan-Ming Tang and Bao-Liang Lu, Age Classification Combining Contour and Texture Feature, Proceedings of 17th International Conference on Neural Information Processing (ICONIP2010), Sydney, Australia, November 22-25, 2010. [pdf]
  2. Tian-Xiang Wu and Bao-Liang Lu, Multi-view Gender Classification Using Hierarchical Classifiers Structure, Proceedings of 17th International Conference on Neural Information Processing (ICONIP2010), Sydney, Australia, November 22-25, 2010. [pdf]
  3. Qi Kong, Bao-Liang Lu, Adaptive Ensemble Learning Strategy Using an Assistant Classifier for Large-scale Imbalanced Patent, Proceedings of 17th International Conference on Neural Information Processing (ICONIP2010), Sydney, Australia, November 22-25, 2010. [pdf]
  4. Xuezhe Ma, Xiaotian Zhang, Hai Zhao and Bao-Liang Lu, Dependency Parser for Chinese Constituent Parsing, CIPS-SIGHAN-2010, August, 2010, Beijing, China. [pdf]
  5. Jia-Xin Ma, Li-Chen Shi and Bao-Liang Lu, Vigilance Estimation by Using Electrooculographic Features, Proceedings of 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010), pp. 6591-6594, Buenos Aires, Argentina, August 31- September 4, 2010.. [pdf]
  6. Li-Chen Shi, Bao-Liang Lu: Off-Line and On-Line Vigilance Estimation Based on Linear Dynamical System and Manifold Learning, Proc. of 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010), pp. 6587-6590, Buenos Aires, Argentina, August 31- September 4, 2010. [pdf]
  7. Georg Bartels, Li-Chen Shi, Bao-Liang Lu: Automatic Artifact Removal from EEG - a Mixed Approach Based on Double Blind Source Separation and Support Vector Machine, Proceedings of 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010), pp. 5383-5386, Buenos Aires, Argentina, August 31- September 4, 2010. [pdf]
  8. Hongbin Yu, Hongtao Lu, Tian Ouyang, Hongjun Liu, Bao-Liang Lu: Vigilance Detection Based on Sparse Representation of EEG, Proceedings of 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010), pp. 2439-2442, Buenos Aires, Argentina, August 31- September 4, 2010. [pdf]
  9. Xiao-Chen Lian, Zhiwei Li, Bao-Liang Lu, Lei Zhang: Max-Margin Dictionary Learning for Multiclass Image Categorization, Proceedings of the 11th Europen Conference on Computer Vision (ECCV2010), Hersonissos, Heraklion, Crete, Greece, September 5-11, 2010. [pdf]
  10. Shaodian Zhang, Hai Zhao, Guodong Zhou and Bao-Liang Lu, Hedge Detection and Scope Finding by Sequence Labeling with Procedural Feature Selection, CoNLL-2010, pp.92-99, Uppsala, Sweden, July 15-16, 2010. [pdf]
  11. Cong Hui, Hai Zhao, Yan Song, Bao-Liang Lu, An Empirical Study on Development Set Selection Strategy for Machine Translation Learning, WMT-2010, pp.67-71, Uppsala, Sweden, July 15-16, 2010. [pdf]
  12. Jian Zhang, Hai Zhao, and Bao-Liang Lu, A Comparative Study on Two Large-Scale HierarchicalText Categorization Tasks' Solutions, Proceedings of International Workshop on Web Information Processing (IWWIP2010), Qingdao, China, July 11-14, 2010. [pdf]
  13. Xiao-Lin Wang and Bao-Liang Lu, Flatten hierarchies large-scale hierarchical text categorization, Proceedings of Fifth International Conference on Digital Information Management (ICDIM2010), pp. 139-144, Thunder Bay, Canada, July 5-8, 2010. [pdf]
  14. Tianqi Zhang, Bao-Liang Lu: Selecting Optimal Orientations of Gabor Wavelet Filters for Facial Image Analysis. Proceedings of International Conferenvce on Image and Signal Processing (ICISP 2010), pp. 218-227, Toris-Rivieres, Canada, June 30-July 2, 2010. [pdf]
  15. Mu Li, James Kwok, and Bao-Liang Lu. Making Large-Scale Nyström Approximation Possible. Proceedings of the Twenty-Seventh International Conference on Machine Learning (ICML 2010), Haifa, Israel, June 21-24, 2010. [pdf]
  16. Gang Jin, Qi Kong, Jian Zhang, Xiaolin Wang, Cong Hui, Hai Zhao, and Bao-Liang Lu, Multiple Strategies for NTCIR-08 Patent Mining at BCMI, Proceedings of the 8th NTCIR Workshop Meeting on Evaluation of Information Access Technologies: Information Retrieval, Question Answering, and Cross-Lingual Information Access, Tokyo, Japan, June 15-18, 2010. [pdf]
  17. Mu Li, James Kwok, and Bao-Liang Lu. Online multiple instance learning with no regret. Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR 2010), San Francisco, CA, USA, June 13-18, 2010. [pdf]
  18. Xiao-Chen Lian, Zhiwei Li, Changhu Wang, Bao-Liang Lu, Lei Zhang: Probabilistic Models for Supervised Dictionary Learning, Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR 2010), San Francisco, CA, USA, June 13-18, 2010. [pdf]
  19. Minzhang Huang, Hai Zhao, Bao-Liang Lu: Pruning Training Samples Using a Supervised Clustering Algorithm, Lecture Notes in Computer Science, vol. 6064, pp. 250-257, 2010. [pdf]
  20. Wen-Yun Yang, James T. Kwok, Bao-Liang Lu: Spectral and Semidefinite Relaxation of the CLUHSIC Algorithm. Proceedings of 2010 SIAM International Conference on Data Mining (SDM 2010), pp. 106-117, April 29-May 1, 201. [pdf]
  21. Yang Yang, Bao-Liang Lu: Protein Subcellular Multi-Localization Prediction Using a Min-Max Modular Support Vector Machine. International Journal of Neural Systems, vol. 20, no. 1, pp. 13-28, 2010. [pdf]
  22. Hai Zhao, Changning Huang, Mu Li, Bao-Liang Lu: A Unified Character-Based Tagging Framework for Chinese Word Segmentation. ACM Transactions on Asian Language Information Processing (TALIP), vol. 9, no. 2, pp. 1-32, 2010. [pdf]

2009

  1. Jing Li and Bao-Liang Lu, “An Adaptive Image Euclidean Distance”, Pattern Recognition, vol. 42, pp. 349-357, 2009. [pdf]
  2. Yun Li and Bao-Liang Lu, “Feature selection based on loss margin of nearest neighbor classification”, Pattern Recognition, vol. 42, pp. 1914-1921, 2009. [pdf]
  3. Dandan Song, Yang Yang, Bin Yu, Binglian Zheng, Zhidong Deng, Bao-Liang Lu, Xuemei Chen, Tao Jiang: Computational prediction of novel non-coding RNAs in Arabidopsis thaliana. BMC Bioinformatics, vol. 10 (S-1), S-36, 2009. [pdf]
  4. Bao-Liang Lu, Xiao-Lin Wang, and Masao Utiyama, “Incorporating prior knowledge into learning by dividing training data”, Frontiers of Computer Science, vol. 3, No. 1, pp. 109-122, 2009. [pdf]
  5. Bao-Liang Lu and Xiao-Lin Wang, “A Parallel and Modular Pattern Classification Framework for Large-Scale Problems”, in C. H. Chen (Ed.), Handbook of Pattern Recognition and Computer Vision (4th Edition), pp. 725-746, World Scientific, 2009. [pdf]
  6. Mu Li and Bao-Liang Lu, “Emotion Classification Based on Gamma-band EEG”, Proceedings of International Conference of the IEEE Engineering in Medicine and Biology Society，pp. 1323-1326, Mineapolis, USA, 2009. [pdf]
  7. Jia-Cheng Guo, Bao-Liang Lu, Zhi-Wei Li, and Lei Zhang, "LogisticLDA: Regularizing Latent Dirichlet Allocation by Logistic Regression", in Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, pp. 160-169, Hong Kong, 2009. [pdf]
  8. Wei-Ming Liang, Chang-Ning Huang, Mu Li and Bao-Liang Lu, “Extracting Keyphrases from Chinese News Articles Using TextRank and Query Log Knowledge”, Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, pp. 733-740, Hong Kong, 2009. [pdf]
  9. Shaojie Shi, Bao-Liang Lu,“EEG Signal Classification during Listening to Native and Foreign Languages Songs”, 4th International IEEE EMBS Conference on Neural Engineering, 2009, Antalya, Turkey, pp. 440-443, 2009. [pdf]
  10. Zheng Ji and Bao-Liang Lu, “Gender Classification Based on Support Vector Machine with Automatic Confidence”, in Proceedings of the 16th International Conference on Neural Information Processing, pp. 685-692, Bangkok, Thailand, 2009. [pdf]
  11. Chao Ma, Bao-Liang Lu, and Masao Utiyama, “Incorporating prior knowledge into task decomposition for large-scale patent classification”, Proceedings of 6th International Symposium on Neural Networks, LNCS5552, pp. 784-793, 2009. [pdf]
  12. Yang Yang and Bao-Liang Lu, “Prediction of protein subcellular multi-localization by using a min-max modular support vector machine”, Yu and Sanchez (Eds.), Advances in Computational Intelligence, AISC 61, pp. 133-143, Springer, 2009. [pdf]
  13. Yue Wang, Bao-Liang Lu, and Zhi-Fei Ye, “Module combination based on decision tree in min-max modular network, Proceedings of International Conference on Neural Computation, pp. 555-558, Madeira, Portugal, 2009. [pdf]
  14. Zheng Ji, Xiao-Chen Lian, and Bao-Liang Lu, " Gender Classification by Fusion of Face and Hair Feature”, Marid and Chacon (Eds.), State of the Art in Face Recognition, In-Teh, Vienna, Austria，pp. 215-230, 2009. [pdf]
  15. Jia-Wei Fu, Li-Chen Shi, and Bao-Liang Lu, “A survey on EEG-based vigilance analysis and estimation”, Chinese Journal of Biomedical Engineering (in Chinese), vol. 28, no. 4, pp. 589-596, 2009. [pdf]
  16. Zhi-Fei Ye, Yi-Min Wen, and Bao-Liang Lu, “A survey of imbalanced pattern classification problems”, CAAI Transactions on Intelligent Systems (in Chinese), vol. 4, no. 2, pp. 148-156, 2009. [pdf]
  17. He Sun and Bao-Liang Lu, “Gender Classification Based on Local Gabor Binary Mapping and Support Vector Machine”, Computer Engineering (in Chinese), vol. 35(2), pp. 210-213, 2009. [pdf]

2008

  1. L.C. Shi, B. L. Lu, "Dynamic Clustering for Vigilance Analysis Based on EEG", 30-th Annual International Conference of the Engineering in Medicine and Biology Society, 2008,Vancouver,British Columbia,Canada, pp. 54-57 [pdf]
  2. M. Li, G.W. Fu, B. L. Lu, "Estimating Vigilance in Driving Simulation using Probabilistic PCA"，30-th Annual International Conference of the Engineering in Medicine and Biology Society, 2008, Vancouver, British Columbia, Canada, pp. 5000-5003 [pdf]
  3. X. Chu, C. Ma, J. Li, B. L. Lu, M. Utiyama and H. Isahara, "Large-Scale Patent Classification with Min-Max Modular Support Vector Machines", Proc. of International Joint Conference on Neural Networks (IJCNN), vol.1, pp.3972-3979, HongKong, China, 2008 [pdf]
  4. B. Xia, H. Sun and B. L. Lu, "Multi-view Gender Classification based on Local Gabor Binary Mapping Pattern and Support Vector Machines", Proc. of International Joint Conference on Neural Networks (IJCNN), vol.1,pp.3388-3395, HongKong, China, 2008 [pdf]
  5. W. Y. Yang and B. L. Lu, "tring kernel framework with feature selection for SVM protein classification", Proc. of the Sixth Asia Pacific Bioinformatics Conference (APBC), vol.6:9-18,Kyoto,Japan, 2008 [pdf]
  6. Y. P. Li and B. L. Lu, "Semantic similarity definition over gene ontology by further mining of the information content", Proc. of the Sixth Asia Pacific Bioinformatics Conference (APBC), vol.6:155-164,Kyoto,Japan, 2008 [pdf]
  7. K. Wu, X. Lin and B. L. Lu, "Cross language text categorization using a bilingual lexicon", Proc. of the Third International Joint Conference on Natural Language Processing (IJCNLP).India, 2008 [pdf]
  8. Yang Yang, B. L. Lu, and Wen-Yun Yang, "Classification of protein sequences based on word segmentation methods", Proc. of the Sixth Asia Pacific Bioinformatics Conference (APBC), vol.6:177-186,Kyoto,Japan, 2008 [pdf]
  9. Cheng. Cong and B. L. Lu, “Partition of Sample Space with Perceptrons”, Computer Simulation (in Chinese), vol. 25(2), pp. 96-99, 2008 [pdf]
  10. Ji-Lin Li and Bao-Liang Lu, “Design of FIR Filters in Complex Domain by Neural Networks”, Computer Simulation (in Chinese), vol. 25(2), pp. 175-177, 2008 [pdf]

2007

  1. J. Luo, Y. Ma,E. Takikawa, S. H Lao, M. Kawade and B. L. Lu. "Person-specific SIFT features for face recognition", International Conference on Acoustic, Speech and Signal Processing (ICASSP2007), vol.2, pp.593-596,Hawaii, 2007. [pdf]
  2. K. Wu, B. L. Lu, "A probabilistic approach to feature selection for multi-class text categorization", Proc. of Fourth International Symposium Neural Networks (ISNN 2007), LNCS, Vol.4491: 1310-1317,Nanjing,China, 2007. [pdf]
  3. K. Wu, B. L. Lu, Masao Uchiyama, and Hitoshi Isahara, "An empirical comparison of min-max-modular k-NN with different voting methods to large-scale text categorization", Soft Computing - A Fusion of Foundations, Methodologies and Applications, vol.12, no.7, pp.647-655, 2008. (ISNN 2007) [pdf]
  4. Y. M. Wen, and B. L. Lu, "A confident majority voting strategy for parallel and modular support vector machines", Proc. of Fourth International Symposium Neural Networks (ISNN 2007), LNCS, Vol. 4493: 525-534, Nanjing, China, 2007. [pdf]
  5. Y. M. Wen and B. L. Lu, "Incremental Learning of Support Vector Machines by Classifier Combining", Proc. of 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007), LNCS, vol.4426: 904-911, Nanjing, China, 2007. [pdf]
  6. K. Wu and B. L. Lu, "Cross-Lingual Document Clustering", Proc. of 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007), LNCS, vol.4426: 956-963, Nanjing, China, 2007. [pdf]
  7. Li-Chen Shi, Hong Yu, and Bao-Liang Lu, "Semi-Supervised Clustering for Vigilance Analysis Based on EEG," Proceedings of International Joint Conference on Neural Networks(IJCNN2007), pp. 1518-1523,Orlando,US, Aug. 2007. [pdf]

2006

  1. W. Y. Yang, B. L. Lu and Y. Yang, "A comparative study on feature extraction from protein sequences for subcellular localization prediction", Proceedings of 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2006)，Toronto，Canada，2006. [pdf]
  2. K. Chen, B. L. Lu and J. Kwok, "Efficient classification of multi-label and imbalanced data using min-max modular classifiers", Proc. of IEEE World Congress on Computation Intelligence, pp.1770-1775,Vancoudar,Canada, July 16-21, 2006.
  3. J. Li and B. L. Lu, "A new supervised clustering algorithm based on min-max modular network with gaussian-zero-crossing functions", Proc. of 2006 IEEE World Congress on Computation Intelligent, pp.786-793,Vancouver,Canada, July 16-21, 2006. [pdf]
  4. Y. Li, B. L. Lu and Z. F. Wu, "A hybrid method of unsupervised feature selection based on ranking". International Conference on Pattern Recognition 2006 (ICPR2006), Vol.2, pp.687-690, Hongkong, August 20-24, 2006. [pdf]
  5. H. C. Lian, B. L. Lu, "Multi-view gender classification using local binary patterns and support vector machines", Lecture Notes in Computer Science, 3972, Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II, 2006, pp.202-209. [pdf]
  6. J. Luo, B. L. Lu, "Gender recognition using a min-max modular support vector machine with equal clustering", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3972, Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II, 2006, pp.210-215. [pdf]
  7. Y. Yang, B. L. Lu, "Prediction of protein subcellular multi-locations with a min-max modular support vector machine", Lecture Notes in Computer Science, 3973,Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III, 2006, pp. 667-673. [pdf]
  8. H. Zhao, B. L. Lu, "A modular reduction method for k-NN algorithm with self-recombination learning", Lecture Notes in Computer Science, 3971, Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings, 2006, pp.537-544. [pdf]
  9. Y. Li, B. L. Lu, Z. F. Wu. "Hierarchical Fuzzy Filter Method for Unsupervised Feature Selection". Journal of Intelligent and Fuzzy Systems, vol. 18 (2), pp. 157-169, 2007 [pdf]
  10. Z. G. Fan and B. L. Lu, "Fast Learning for Statistical Face Detection", ICONIP2006, Lecture Notes in Computer Science, LNCS 4233, pp. 187-196, 2006. [pdf]
  11. Hong Shen, B. L. Lu, Masao Utiyama and Hitoshi Isahara, "Comparison and Improvements of Feature Extraction Methods for Text Categorization", Computer Simulation (in Chinese), vol. 23(3), pp. 222-224, 2006 [pdf]

2005

  1. Z. G. Fan and B. L. Lu, "Fast Recognition of Multi-View Faces with Feature Selection", 10th IEEE International Conference on Computer Vision, Beijing (ICCV'05), vol. 1, pp. 76-81, 2005. [pdf]
  2. H. C. Lian and B. L. Lu, "An algorithm for pruning redundant modules in min-max modular network", International Joint Conference on Neural Networks (IJCNN2005), pp.1983-1988, Montréal,Québec,Canada, July 31-August 4, 2005 [pdf]
  3. F. Y. Liu, K. Wu, H. Zhao, and B. L. Lu, "Fast text categorization with min-max modular support vector machines", International Joint Conference on Neural Networks (IJCNN2005), Vol. 1, pp.570-575, Montréal, Québec, Canada, July 31-August 4, 2005 [pdf]
  4. Y. Yang and B. L. Lu, "Extracting features from protein sequences using chinese segmentation techniques for subcellular localization", Proceedings of 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2005), pp. 288-295, San Diego, California, USA, November 14-15, 2005. [pdf]
  5. F. Y. Liu, K, A. Wang, B. L. Lu, M. Utiyama, and H. Isahara, "Efficient Text Categorization Using a Min-Max Modular Support Vector Machine"，Human Interaction With Machine, Hommel, G and Shen H. Y. Eds.,Springer, pp. 13-22, 2005. [pdf]
  6. H. Zhao, B. L. Lu, "Improvement on Response Performance of Min-Max Modular Classifier by Symmetric Module Selection", Proceedings of Second International Symposium Neural Networks (ISNN’05), LNCS, Vol. 3497: 39-44,Chongqing,China, 2005. [pdf]
  7. J. Li, B. L. Lu, "Typical Sample Selection and Redundancy Reduction for Min-Max Modular Network with GZC Function", Proceedings of Second International Symposium on Neural Networks (ISNN’05), LNCS, Vol. 3496, pp. 467-472 Chongqing, China, 2005. [pdf]
  8. Y. M. Wen and B. L. Lu, "A hierarchical and parallel method for training support vector machines", Proceedings of Second International Symposium on Neural Networks (ISNN’05), LNCS, 881-886,Chongqing,China, 2005 [pdf]
  9. Y. Yang, B. L. Lu, "Structure Pruning Strategies for Min-Max Modular Network", Proceedings of Second International Symposium on Neural Networks (ISNN’05), LNCS, vol. 3496, pp. 646-651, Chongqing, China, 2005. [pdf]
  10. K. A. Wang, H. Zhao and B. L. Lu, "Task Decomposition Using Geometric Relation for Min-Max Modular SVMs", Proceedings of Second International Symposium on Neural Networks (ISNN’05), LNCS, vol. 3496, pp. 887-892, Chongqing, China, 2005. [pdf]
  11. H. Zhao and B. L. Lu, "A General Procedure for Combining Binary Classifiers and Its Performance Analysis", Proceeding s of the First International Conference on Natural Computation (ICNC'05), LNCS, vol. 3610, pp. 303-312, Changsha, China, 2005. [pdf]
  12. J. Li, B. L. Lu, and M. Ichikawa, "An algorithm for pruning redundant modules in min-max modular network with GZC function", Proceedings of the First International Conference on Natural Computation (ICNC'05), LNCS, vol.3610, pp.293-302, Changsha, China, 2005. [pdf]
  13. Z. G. Fan and B. L. Lu, "Multi-View Face Recognition with Min-Max Modular SVMs", Proceedings of the First International Conference on Natural Computation (ICNC'05), LNCS, vol. 3611, pp. 396-399,Changsha,China, 2005. [pdf]
  14. H. C. Lian and B. L. Lu, "Gender Recognition Using a Min-Max Modular SVM", Proceedings of the First International Conference on Natural Computation (ICNC'05), LNCS, vol. 3611, pp. 433-436, Changsha, China, 2005. [pdf]
  15. Y. K. Chen, H. Zhao and B. L. Lu, "On improvement on generalization performance of classifier by using empirical risk", 2nd International Conference on Neural Networks and Brain, vol.1 pp. 41-46,Beijing,China, 2005. [pdf]
  16. H. C. Lian and B. L. Lu, "Age estimation using a min-max modular support vector machine", 12th International Conference on Neural Information Processing (ICONIP 2005), pp.83-88,Taipei, October 30-November 2, 2005. [pdf]
  17. Y. M. Wen, B. L. Lu and H. Zhao, "Equal clustering makes min-max modular support vector machine more efficient", 12th International Conference on Neural Information Processing (ICONIP 2005), pp.77-82, Taipei, October 30-November 2, 2005 [pdf]
  18. H. Zhao, B. L. Lu, Y. M. Wen and K. A. Wang, "On effective decomposition of training data sets for min-Max modular classifier", 12th International Conference on Neural Information Processing (ICONIP 2005), pp.343-348, Taipei, October 30-November 2, 2005 [pdf]
  19. H. Zhao and B. L. Lu, "Determination of hyperplane by PCA for dividing training data set", 12th International Conference on Neural Information Processing (ICONIP 2005), pp.755-760, Taipei, October 30-November 2, 2005
  20. Yi-Min Wen, Yang Yang and Bao-Liang Lu, “Research on the Application of Ensemble Learning Algorithms to Incremental Learning”, Journal of Computer Research and Development (in Chinese), vol. 42, Suppl. B, pp. 222-227, 2005 [pdf]
  21. Hai Zhao and Bao-Liang Lu, “A Self Recombination Learning Algorithm for Min-Max Combining Classifier”, Journal of Computer Research and Development (in Chinese), vol. 42, Suppl. B, pp. 243-247, 2005 [pdf]
  22. Zhi-Gang Fan and Bao-Liang Lu, “Fast recognition of Multi-View Faces Based on Iterative Feature Selection”, Journal of Computer Research and Development (in Chinese), vol. 42, Suppl. B, pp. 325-329, 2005 [pdf]
  23. Bao-Liang Lu, Feng-Yao Liu, Masao Utiyama, and Hitoshi Isahara, “Multilabel Text categorization Using a Min-Max Modular Support Vector Machine”, Journal of Computer Research and Development (in Chinese), vol. 42, Suppl. B, pp. 361-366, 2005 [pdf]
  24. Yi-Min Wen, Yang Yang and Bao-Liang Lu, “Improvement Research of Min-Max Modular Support Vector Machine”, Computer Engineering and Applications (in Chinese), vol. 19, pp. 185-188, 2005 [pdf]

2004

  1. B. L. Lu, K. A. Wang, M. Utiyama, and H. Isahara, "A part-versus-part method for massively parallel training of support vector machines", Proc. of IEEE/INNS Int. Joint Conf. on Neural Networks (IJCNN2004), Budabest, Hungary, July 25-29, pp. 735-740, 2004. [pdf]
  2. Z. G. Fan and B. L. Lu, "An adjusted Gaussian skin-color model based on principle component analysis", Advances in Neural Networks-ISNN2004, Lecture Notes in Computer Science, vol. 3173, part I, pp. 804-809, 2004. [pdf]
  3. Y. M. Wen and B. L. Lu, "A cascade method for reducing training time and the number of support vectors", Advances in Neural Networks-ISNN2004, Lecture Notes in Computer Science, vol. 3173, part I, pp. 480-485, 2004. [pdf]
  4. H. Zhao and B. L. Lu, "Analysis of fault tolerance of a combining classifier", Advances in Neural Networks-ISNN2004, Lecture Notes in Computer Science, vol. 3173, 888-893, 2004. [pdf]
  5. B. Huang and B. L. Lu, "Fault diagnosis for industrial images using a min-max modular neural network", 11th International Conference on Neural Information Processing (ICONIP2004), Calcutta, India, Nov. 22-25, 2004, Lecture Notes in Computer Science, vol. 3316, 843-847, 2004. [pdf]
  6. Z. G. Fan and B. L. Lu, "Feature selection for fast image classification with support vector machines", 11th International Conference on Neural Information Processing (ICONIP2004), Calcutta, India, Nov. 22-25, 2004, Lecture Notes in Computer Science, vol. 3316, 1026-1031, 2004. [pdf]
  7. H. Zhao and B. L. Lu, "A modular k-nearest neighbor classification method for massively parallel text categorization", First International Symposium on Computational and Information Science, Shanghai, Dec. 16-18, 2004, Lecture Notes in Computer Science, vol. 3314, 867-872, 2004. [pdf]
  8. B. L. Lu, K. A. Wang, and Y. M. Wen, "Comparison of parallel and cascade methods for training support vector machines on large-scale problems" (invited paper), Proc. Of International Conference on Machine Learning and Cybernetics (ICMLC04), pp.3056-3061,Shanghai,China,Aug. 26-29, 2004 [pdf]
  9. B. L. Lu, J. H. Shin, M. Ichikawa, 2004. Massively parallel classification of single-trial EEG signals using a min-max modular neural network. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 51 (3): 551-558. [pdf]

2003

  1. B. L. Lu and K. Ito, "Converting general nonlinear programming problems into separable programming problems with feedforward neural networks", Neural Networks, vol. 16, pp. 1059-1074, 2003. [pdf]
  2. B. L. Lu, Q. Ma, M. Ichikawa, and H. Isahara, "Efficient part-of-speech tagging with a min-max modular neural network", Applied Intelligence, vol 19, pp. 65-81, 2003. [pdf]

2002 and before

  1. B. L. Lu, M. Ichikawa, "Emergent on-line learning with a Gaussian zero-crossing discriminant function", Proceedings of the International Joint Conference on Neural Networks, Honolulu, 2002, 2, pp. 1263-1268. [pdf]
  2. Q. Ma, B. L. Lu, H. Isahara, M. Ichikawa, "Part of speech tagging with min-max modular neural networks", Systems and Computers in Japan, 2002, 33(7), pp.30-39. [pdf]
  3. B. L. Lu, J. Shin, M. Ichikawa, "Massively parallel classification of EEG signals using min-max modular neural networks", Lecture Notes in Computer Science, vol. 2130, 601-608, 2001. [pdf]
  4. Q. Ma, B. L. Lu, M. Murata, et al. "On-line error detection of annotated corpus using modular neural networks", Lecture Notes in Computer Science, vol. 2130, 1185-1192, 2001. [pdf]
  5. J. H. Shin, B. L. Lu, A. Talnov, et al. "Readingauditory discrimination behaviour of freely moving rats from hippocampal EEG", Neurocomputing, vol. 38, 1557-1566, 2001. [pdf]
  6. B. L. Lu, M. Ichikawa, "Emergence of learning: An approach to coping with NP-complete problems in learning", Proceedings of the International Joint Conference on Neural Networks, Como, 2000, 4, pp.159-164. [pdf]
  7. B. L. Lu, M. Ichikawa, S. Hosoe, "A modular massively parallel learning framework for brain-like computers", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Tokyo, 1999, 5, pp. 332-337. [pdf]
  8. Q. Ma, B. L. Lu, H. Isahara, "Part of speech tagging with min-max modular neural networks", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Tokyo, 1999, 5, pp.356-360. [pdf]
  9. G. R. Ji, B. L. Lu, X. Chen, J. Wang, "Object searching in scale-space", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Tokyo, 1999, 1, pp.565-570. [pdf]
  10. B. L. Lu, H. Kita, Y. Nishikawa, "Inverting feedforward neural networks using linear and nonlinear programming", IEEE Transactions on neural networks, vol. 10 (6), 1271-1290, 1999. [pdf]
  11. B. L. Lu, M. Ito, "Task decomposition and module combination based on class relations: A modular neural network for pattern classification", IEEE Transactions on Neural Networks, vol. 10 (5), 1244-1256, 1999. [pdf]
  12. B. L. Lu, M. Ito, "Task decomposition based on class relations: A modular neural network architecture for pattern classification", Lecture Notes in Computer Science, vol. 1240, 330-339, 1997. [pdf]
  13. Book Chapters: B. L. Lu and K. Ito, "Transformation of nonlinear programming problems into separable ones using multi-layer neural networks", Mathematics of Neural Networks: Models, Algorithms and Applications, S. W. Ellacott, J. C. Mason, and I. J. Anderson Eds., Kluwer Academic Publishers, Norwell, pp. 235-239, 1997 [pdf]
  14. B. L. Lu, K. Ito, M. Ito, "Solving inverse kinematics problem of redundant manipulators in an environment with obstacles using separable nonlinear programming", Proceedings of the Japan/USA Symposium on Flexible Automation, Boston, 1996, 1, pp.79-82.
  15. B. L. Lu, K. Ito, "A Parallel and modular multi-sieving neural network architecture with multiple control networks", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Beijing, 1996, 2, pp. 1303-1308. [pdf]
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