李小伟

李小伟

博士,副教授。E-mail: lixwei@lzu.edu.cn

美国计算机协会(ACM)、英国工程技术学会(IET)、中国计算机学会(CCF)会员。UAIS实验室脑电与眼动数据处理组组长。

研究方向为数据挖掘,情感计算。当前研究主要为抑郁症患者脑电信号、眼动信号分析处理。参与多项国家级及省部级项目,累计科研经费进账100余万元。发表SCI/EI 索引论文20余篇,主要项目及论文如下:

科研项目

  • 自然科学基金重点项目, 61632014, 基于注意神经机制的可计算模型研究, 2017.01-2021.12
  • 国家“973”计划”, 2014CB744600, 基于生物、心理多模态信息的潜在抑郁风险预警理论与生物传感关键技术研究, 2014.1-2018.12
  • 国家自然科学基金重大项目, 61210010, 基于生物信息反馈的普适心理干预关键问题的研究, 2013.01-2017.12
  • 科技部国际(地区)合作交流项目, 2013DFA11140, 基于生理反馈的普适心理干预关键问题及应用合作研究, 2012.01-2015.12
  • 国家“973”计划”, 2011CB711000, 面向长期空间飞行的航天员作业能力变化规律及机制研究, 2011.01-2015.12
  • EU Seventh Framework Programme (FP7-ICT-2009-4), Project number:248544, 5129044 EUROS, 2009.1-2014.12
  • “中央高校基本科研业务费专项资金”自由探索面上项目, 基于生物电信号的网络用户情感特征模型的研究, lzujbky-2011-64, 2011.1-2012.12
  • 自然科学基金面上项目, 60973138, 面向普适计算的针对生物电信号上下文感知的研究, 2010.01-2012.12

 

发表论文:

  • Li X, Hu B, Zhu T, et al. Towards affective learning with an EEG feedback approach[C]//Proceedings of the first ACM international workshop on Multimedia technologies for distance learning. ACM, 2009: 33-38. (EI)
  • Li Xiaowei, Li Haibo, Li Yongli, Lin He. Reduction Algorithm and Directed Graph. Proceeding of the 11th Joint International Computer conference, World Scientific Publishing.625-628. 10,2005. (EI)
  • Xiaowei Li, Bin Hu, Qinglin Zhao, Li Liu, Hong Peng, Yanbing Qi, Chengsheng Mao. Improve Affective Learning With EEG Approach. Computing and Informatics, Vol.29, 2010, p.557-570. (SCI)
  • Yan, J., Hu, B., Zhang, H., Zhu, T., & Li, X. Forbidden subgraph and perfect path-matchings. (2009) The 1th IEEE Symposium on Web Society, 23-24, August, 2009, Lanzhou, China. IEEE Press. (EI)
  • Zhu, T. – Hu , B. – Yan, J. – Li, X.Semi-Supervised Learning for Personalized Web Recommender System.Computing and Informatics, Vol.29, 2010, p. 617-627. (SCI)
  • Li, Y. and Li, X. and Ratcliffe, M. and Liu, L. and Qi, Y. and Liu, Q., (2011) A real-time EEG-based BCI System for attention recognition in ubiquitous environment,  Proceedings of 2011 international workshop on Ubiquitous affective awareness and intelligent interaction, Ubicomp 2011 pp.33-40. (EI)
  • Li, Xiaowei; Hu, Bin; Dong, Qunxi; Campbell, William; Moore, Philip; Peng, Hong. EEG-based attention recognition. Proceedings – 2011 6th International Conference on Pervasive Computing and Applications, ICPCA 2011, p 196-201(EI)
  • Wan J, Hu B, Li X. EEG: A Way to Explore Learner’s Affect in Pervasive Learning Systems[M]//Advances in Grid and Pervasive Computing. Springer Berlin Heidelberg, 2010: 109-119. (EI)
  • Zhao G, Hu B, Li X, et al. A Pervasive Stress Monitoring System Based on Biological Signals[C]//Intelligent Information Hiding and Multimedia Signal Processing, 2013 Ninth International Conference on. IEEE, 2013: 530-534. (EI)
  • Dong, Q., Hu, B., Zhang, J., Li, X., & Ratcliffe, M. (2013, August). A study on visual attention modeling—A linear regression method based on EEG. InNeural Networks (IJCNN), The 2013 International Joint Conference on (pp. 1-6). IEEE. (EI)
  • Li B, Hu B, Li X, et al. A Study on Attention Allocation of Psychological Distress Students Based on Eye Movement Data Analysis[C]//International Conference on Human Centered Computing. Springer International Publishing, 2014: 104-114. (EI)
  • Li X, Hu B, Xu T, et al. A study on EEG-based brain electrical source of mild depressed subjects[J]. Computer methods and programs in biomedicine, 1 2 0 ( 2 0 1 5 ) 135–141. (SCI)
  • Li X, Hu B, Shen J, et al. Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks[J]. Journal of medical systems, 2015, 39(12): 187. (SCI)
  • Li X, Hu B, Sun S, et al. EEG-based mild depressive detection using feature selection methods and classifiers[J]. Computer Methods and Programs in Biomedicine, 2016, 136: 151-161. (SCI)
  • Li X, Cao T, Sun S, et al. Classification study on eye movement data: Towards a new approach in depression detection[C]//Evolutionary Computation (CEC), 2016 IEEE Congress on. IEEE, 2016: 1227-1232. (EI)
  • Li X, Cao T, Hu B, et al. EEG Topography and Tomography (sLORETA) in Analysis of Abnormal Brain Region for Mild Depression[C]//International Conference on Brain and Health Informatics. Springer International Publishing, 2016: 304-311. (EI)
  • Hu B, Li X, Sun S, et al. Attention Recognition in EEG-Based Affective Learning Research Using CFS+ KNN Algorithm [J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, PP(99):1-1, 2016. (SCI)
  • Li X, Jing Z, Hu B, et al. An EEG-based study on coherence and brain networks in mild depression cognitive process[C]//Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on. IEEE, 2016: 1275-1282.(ccf B)
  • Zhu, J., Han, X., Ma, R., Li, X., Cao, T., Sun, S., & Hu, B. (2016, January). Exploring user mobile shopping activities based on characteristic of eye-tracking. In International Conference on Human Centered Computing (pp. 556-566). Springer International Publishing. (EI)
  • Li X, Jing Z, Hu B, et al. A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering[J]. Complexity, 2017, 2017. (SCI)
  • Hu B, Rao J, Li X, et al. Emotion Regulating Attentional Control Abnormalities In Major Depressive Disorder: An Event-Related Potential Study[J]. Scientific reports, 2017, 7(1): 13530.(SCI)

Xiaowei Li

Associate Professor , Ph.D

Address: No.222 , TianShui  Road(south) ,ChengGuan District,LanZhou City, GanSu Province, China

Zip Code: 730000

  Telephone: (+86931) 8912778

  E-mail:    Lixwei@lzu.edu.cn

 

Research Interests

My research interests focus on Ubiquitous Computing and Data Mining. Specifically, I am very interested in: 1) The role of Bio-signals in emotion recognition. 2) The application of data mining method in the field of bio-signals processing.3) HCI.

 

Projects

  1. Variation and mechanism of operating capability of astronauts in long-term space flight (funded by 973 Program Project of China) : Project number:
  2. Online Predictive Tools for Intervention in Mental Illness (OPTIMI) (funded by the EU Framework  Seven Project) FP7-ICT-2009-4) Project number:248544
  3. Pervasive Computing Context Awareness on Bioelectrical signal(funded by National Science Foundation of China) Project number:60973138
  4. Modelling of web user’s affect features based on bio-signals (funded by Lanzhou University) Project number:860535
  5. Reseach on alarming theory of potential depression risk and key technology of bio-sensing based on biological and psychological multimodal information(funded by 973 Program Project of China)
  6. Variation and mechanism of operating capability of astronauts in long-term space flight (funded by 973 Program Project of China)
  7. Research on key problems of pervasive psychological intervention based on Biological information feedback (funded by International (regional) cooperation projects of National Science Foundation of China)

 

 

 Publication

  • Li X, Hu B, Zhu T, et al. Towards affective learning with an EEG feedback approach[C]//Proceedings of the first ACM international workshop on Multimedia technologies for distance learning. ACM, 2009: 33-38. (EI)
  • Li Xiaowei, Li Haibo, Li Yongli, Lin He. Reduction Algorithm and Directed Graph. Proceeding of the 11th Joint International Computer conference, World Scientific Publishing.625-628. 10,2005. (EI)
  • Xiaowei Li, Bin Hu, Qinglin Zhao, Li Liu, Hong Peng, Yanbing Qi, Chengsheng Mao. Improve Affective Learning With EEG Approach. Computing and Informatics, Vol.29, 2010, p.557-570. (SCI)
  • Yan, J., Hu, B., Zhang, H., Zhu, T., & Li, X. Forbidden subgraph and perfect path-matchings. (2009) The 1th IEEE Symposium on Web Society, 23-24, August, 2009, Lanzhou, China. IEEE Press. (EI)
  • Zhu, T. – Hu , B. – Yan, J. – Li, X.Semi-Supervised Learning for Personalized Web Recommender System.Computing and Informatics, Vol.29, 2010, p. 617-627. (SCI)
  • Li, Y. and Li, X. and Ratcliffe, M. and Liu, L. and Qi, Y. and Liu, Q., (2011) A real-time EEG-based BCI System for attention recognition in ubiquitous environment,  Proceedings of 2011 international workshop on Ubiquitous affective awareness and intelligent interaction, Ubicomp 2011 pp.33-40. (EI)
  • Li, Xiaowei; Hu, Bin; Dong, Qunxi; Campbell, William; Moore, Philip; Peng, Hong. EEG-based attention recognition. Proceedings – 2011 6th International Conference on Pervasive Computing and Applications, ICPCA 2011, p 196-201(EI)
  • Wan J, Hu B, Li X. EEG: A Way to Explore Learner’s Affect in Pervasive Learning Systems[M]//Advances in Grid and Pervasive Computing. Springer Berlin Heidelberg, 2010: 109-119. (EI)
  • Zhao G, Hu B, Li X, et al. A Pervasive Stress Monitoring System Based on Biological Signals[C]//Intelligent Information Hiding and Multimedia Signal Processing, 2013 Ninth International Conference on. IEEE, 2013: 530-534. (EI)
  • Dong, Q., Hu, B., Zhang, J., Li, X., & Ratcliffe, M. (2013, August). A study on visual attention modeling—A linear regression method based on EEG. InNeural Networks (IJCNN), The 2013 International Joint Conference on (pp. 1-6). IEEE. (EI)
  • Li B, Hu B, Li X, et al. A Study on Attention Allocation of Psychological Distress Students Based on Eye Movement Data Analysis[C]//International Conference on Human Centered Computing. Springer International Publishing, 2014: 104-114. (EI)
  • Li X, Hu B, Xu T, et al. A study on EEG-based brain electrical source of mild depressed subjects[J]. Computer methods and programs in biomedicine, 1 2 0 ( 2 0 1 5 ) 135–141. (SCI)
  • Li X, Hu B, Shen J, et al. Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks[J]. Journal of medical systems, 2015, 39(12): 187. (SCI)
  • Li X, Hu B, Sun S, et al. EEG-based mild depressive detection using feature selection methods and classifiers[J]. Computer Methods and Programs in Biomedicine, 2016, 136: 151-161. (SCI)
  • Li X, Cao T, Sun S, et al. Classification study on eye movement data: Towards a new approach in depression detection[C]//Evolutionary Computation (CEC), 2016 IEEE Congress on. IEEE, 2016: 1227-1232. (EI)
  • Li X, Cao T, Hu B, et al. EEG Topography and Tomography (sLORETA) in Analysis of Abnormal Brain Region for Mild Depression[C]//International Conference on Brain and Health Informatics. Springer International Publishing, 2016: 304-311. (EI)
  • Hu B, Li X, Sun S, et al. Attention Recognition in EEG-Based Affective Learning Research Using CFS+ KNN Algorithm [J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, PP(99):1-1, 2016. (SCI)
  • Li X, Jing Z, Hu B, et al. An EEG-based study on coherence and brain networks in mild depression cognitive process[C]//Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on. IEEE, 2016: 1275-1282.(ccf B)
  • Zhu, J., Han, X., Ma, R., Li, X., Cao, T., Sun, S., & Hu, B. (2016, January). Exploring user mobile shopping activities based on characteristic of eye-tracking. In International Conference on Human Centered Computing (pp. 556-566). Springer International Publishing. (EI)
  • Li X, Jing Z, Hu B, et al. A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering[J]. Complexity, 2017, 2017. (SCI)
  • Hu B, Rao J, Li X, et al. Emotion Regulating Attentional Control Abnormalities In Major Depressive Disorder: An Event-Related Potential Study[J]. Scientific reports, 2017, 7(1): 13530.(SCI)

 

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