Xiaowei Li Professor , Ph.D
Address: No.222 , TianShui Road(south) ,ChengGuan District,LanZhou City, GanSu Province, China
Zip Code: 730000
Telephone: (+86931) 8912778
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/Depression 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：2011CB71100X.
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 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.
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.
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.
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.
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.
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.
Dong, Q., Li, Y., Hu, B., Liu, Q., Li, X., & Liu, L. (2010, December). A solution on ubiquitous EEG-based biofeedback music therapy. In 5th International Conference on Pervasive Computing and Applications (pp. 32-37). IEEE.
Li Y, Li X, Ratcliffe M, et al. A real-time EEG-based BCI system for attention recognition in ubiquitous environment[C]//Proceedings of 2011 international workshop on Ubiquitous affective awareness and intelligent interaction. ACM, 2011: 33-40.
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.
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. In Neural Networks (IJCNN), The 2013 International Joint Conference on (pp. 1-6). IEEE.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Zhu J, Li J, Li X*, et al. Neural basis of the emotional conflict processing in major depression: ERPs and source localization analysis on the N450 and P300 components[J]. Frontiers in Human Neuroscience, 2018, 12: 214.
Xiaowei Li, Jianxiu Li, Bin Hu, et al. Attentional bias in MDD: ERP components analysis and classification using a dot-probe task. Computer Methods and Programs in Biomedicine. Volume 164, October 2018, Pages 169-179.
Li, Y., Hu, B., Zheng, X., & Li, X. (2019). EEG-Based Mild Depressive Detection Using Differential Evolution. IEEE Access, 7, 7814-7822.
Mao W, Zhu J, Li X*, et al. Resting State EEG Based Depression Recognition Research Using Deep Learning Method[C]//International Conference on Brain Informatics. Springer, Cham, 2018: 329-338.
Sun S, Li X, Zhu J, et al. Graph Theory Analysis of Functional Connectivity in Major Depression Disorder with High-Density Resting State EEG data[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019.
Li X, La R, Wang Y, et al. EEG-based mild depression recognition using convolutional neural network[J]. Medical & biological engineering & computing, 2019: 1-12.
