近五年主持或参加科研项目(课题)及人才计划项目情况:

1.自然科学基金重点项目, 61632014.

2.国家“973”计划, 2014CB744600.

3.国家自然科学基金重大项目, 61210010.

4.科技部国际(地区)合作交流项目, 2013DFA11140.

近期发表论文：

1.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. (通讯作者)

2.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. (通讯作者)

3.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(通讯作者)

4.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. (通讯作者)

5.Shao,X.,et al.,Analysis of Functional Brain Network in MDD based on Improved Empirical Mode Decomposition with Resting State EEG Data. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2021:p.1-1. (通讯作者)

6.S. Sun, H. Chen, X. Shao, L. Liu, X. Li and B. Hu, "EEG Based Depression Recognition by Combining Functional Brain Network and Traditional Biomarkers,"2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020, pp. 2074-2081. (通讯作者)

7.Zhu, J. , Wang, Z. , Gong, T. , Zeng, S. , & Zhang, L. . (2020). An improved classification model for depression detection using eeg and eyetracking data. IEEE Transactions on NanoBioence, PP(99), 1-1.(通讯作者)

8.Y. Fan, R. Yu, J. Li, J. Zhu and X. Li, "EEG-based mild depression recognition using multi-kernel convolutional and spatial-temporal Feature," 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea (South), 2020, pp. 1777-1784. (通讯作者)

9.Li X, La R, Wang Y, Hu B and Zhang X (2020) A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography. Front. Neurosci. 14:192. doi: 10.3389/fnins.2020.00192