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在研项目：

国家自然科学基金委员会面上项目，面向抑郁障碍识别的多模态生理信号神经机制协同融合建模研究(62072219)，主持。

甘肃省自然科学基金重点项目，面向抑郁识别的脑神经动力学隐状态表征构建研究(22JR5RA401)，主持。

中央高校优秀青年支持计划项目，面向情感障碍分类诊断的心理生理动力学模型研究(lzujbky-2022-ey13)，主持。

国家重点研发计划，基于心理生理多模态信息的抑郁障碍早期识别与干预方法(2019YFA0706200)，项目骨干。

已完成项目：

国家自然科学基金委员会青年基金，基于网络行为及生理反馈信息的抑郁风险预测多任务建模(61402211)，主持。

“973”计划项目，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(2014CB744600)，项目骨干。

发表SCI/EI论文40余篇，部分代表性成果如下：

[1] Shen J, Zhang Y, Liang H, et al. Exploring the Intrinsic Features of EEG signals via Empirical Mode Decomposition for Depression Recognition[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022.(IF=4.528)

[2] Li S, Li W, Xing Z, et al. A personality-guided affective brain—computer interface for implementation of emotional intelligence in machines[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(8): 1158-1173. (CCF C类，IF=2.545)

[3] Li R, Ren C, Li C, et al. SSTD: A Novel Spatio-Temporal Demographic Network for EEG-Based Emotion Recognition[J]. IEEE Transactions on Computational Social Systems, 2022. (CCF C类，IF=4.747)

[4] Li R, Ren C, Zhang X, et al. A novel ensemble learning method using multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition[J]. Computers in biology and medicine, 2022, 140: 105080. (IF=6.698)

[5] Wei X, Chen M, Wu M, et al. EEG-Based Depression Detection with a Synthesis-Based Data Augmentation Strategy[C]//Bioinformatics Research and Applications: 17th International Symposium, ISBRA 2021, Shenzhen, China, November 26–28, 2021, Proceedings 17. Springer International Publishing, 2021: 484-496. (CCF C类)

[6] Shen J, Zhang X, Huang X, et al. An optimal channel selection for EEG-based depression detection via kernel-target alignment[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 25(7): 2545-2556. (Top，CCF C类，IF=7.021)

[7] Zhang X, Liu J, Shen J, et al. Emotion recognition from multimodal physiological signals using a regularized deep fusion of kernel machine[J]. IEEE transactions on cybernetics, 2020, 51(9): 4386-4399.(Top，CCF B类，IF=10.387)

[8] Zhang X, Lu D, Pan J, et al. Fatigue detection with covariance manifolds of electroencephalography in transportation industry[J]. IEEE Transactions on Industrial Informatics, 2020, 17(5): 3497-3507.(Top，CCF C类，IF=9.112)

[9] Zhang X, Pan J, Shen J, et al. Fusing of electroencephalogram and eye movement with group sparse canonical correlation analysis for anxiety detection[J]. IEEE Transactions on Affective Computing, 2020, 13(2): 958-971.(CCF B类，IF=6.288)

[10] Shen J, Zhang X, Wang G, et al. An improved empirical mode decomposition of electroencephalogram signals for depression detection[J]. IEEE Transactions on Affective Computing, 2019, 13(1): 262-271. (CCF B类，IF=6.288，ESI高被引论文)

[11] Zhang X, Shen J, ud Din Z, et al. Multimodal Depression Detection: Fusion of Electroencephalography and Paralinguistic Behaviors Using a Novel Strategy for Classifier Ensemble[J]. IEEE journal of biomedical and health informatics, 2019, 23(6): 2265-2275. (Top，CCF C类，IF=7.021)

[12] Zhang X, Lu D, Shen J, et al. Spatial-temporal Joint optimization Network on Covariance Manifolds of Electroencephalography for Fatigue Detection[C]//2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020: 893-900.(CCF B类)

[13] Guo Z, Fu E, Pan J, et al. Anxiety Detection with Nonlinear Group Correlation Fusion of Electroencephalogram and Eye Movement[C]//2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020: 2596-2602. (CCF B类)

[14] Zhang X, Li J, Hou K, et al. EEG-based depression detection using convolutional neural network with demographic attention mechanism[C]//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2020: 128-133.

[15] 基于多导联脑电最优导联选择的抑郁检测系统，国家发明专利，ZL201911156635.X，2022.

International Journal of Data Mining and Bioinformatics (IJDMB)编委，中国中文信息学会情感计算专业委员会(筹)执委，中国人工智能学会智能融合专业委员会委员，中国人工智能学会情感智能专业委员会委员。