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近五年主持或参加科研项目(课题)及人才计划项目情况:

1. 自然科学基金面上项目，62372216，基于脑电信号的抑郁识别关键技术研究;

2. 国家科技部，科技创新2030--“脑科学与类脑研究”重点项目， 2022ZD0208500，新型无创脑机接口：理论、技术与应用示范;

3. 甘肃省科技厅, 甘肃省自然科学基金重点项目，22JR5RA410, 基于脑电源定位的抑郁症患者脑网络模型研究;

4. 自然科学基金重点项目， 61632014， 注意神经机制的可计算模型研究;

5. 科技部, 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。

近期发表论文：

1、Shao, X., Kong, W., Sun, S., Li, N., Li, X., & Hu, B. Analysis of functional connectivity in depression based on a weighted hyper-network method[J]. Journal of Neural Engineering.2023,20(1):016023. 

2、Xuexiao Shao, Danfeng Yan, Wenwen Kong, Shuting Sun, Mei Liao, Wenwen Ou,Yan Zhang, Fang Zheng, Xiaowei Li, Lingjiang Li, Bin Hu. Brain function changes reveal rapid antidepressant effects of nitrous oxide for treatment-resistant depression: Evidence from task-state EEG. Psychiatry Research.2023, 322: 115072. 

3、S. Sun et al., Clustering-Fusion Feature Selection Method in Identifying Major Depressive Disorder Based on Resting State EEG Signals, in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2023.3269814.

4、J. Zhu et al., Mutual Information Based Fusion Model (MIBFM): Mild Depression Recognition Using EEG and Pupil Area Signals, in IEEE Transactions on Affective Computing, doi: 10.1109/TAFFC.2022.3171782. 

5、Li, J., Chen, J., Zhang, Z., Hao, Y., Li, X., and Hu, B.A thresholding method based on society modularity and role division for functional connectivity analysis[J].Journal of Neural Engineering. DOI:10.1088/1741-2552/ac8dc3. 

6、Zhu J, Wei S, Xie X, et al. Content-based multiple evidence fusion on EEG and eye movements for mild depression recognition [J]. Computer Methods and Programs in Biomedicine, 2022, 226: 107100.

7、X. Lin,W. Kong,J. Li,et al. Aberrant Static and Dynamic Functional Brain Network in Depression Based on EEG Source Localization[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics,2022,PP(99):1-14. 

8、H. Chen et al., "Personal-Zscore: Eliminating Individual Difference for EEG-based Cross-Subject Emotion Recognition," in IEEE Transactions on Affective Computing, doi: 10.1109/TAFFC.2021.3137857. 

9、J. Li et al., "Altered Brain Dynamics and Their Ability for Major Depression Detection using EEG Microstates Analysis," in IEEE Transactions on Affective Computing, doi: 10.1109/TAFFC.2021.3139104. 

10、Li JX , Chen JH , Kong WW, Li XW, Hu B. Abnormal core functional connectivity on the pathology of MDD and antidepressant treatment: a systematic review[J]. Journal of Affective Disorders. 2021, doi：https://doi.org/10.1016/j.jad.2021.09.074.

11、Li J , Hao Y , Zhang W , et al. Effective connectivity based EEG revealing the inhibitory deficits for distracting stimuli in major depression disorders[J]. IEEE Transactions on Affective Computing, 2021, PP(99):1-1.