Political Affiliation: Member of the Communist Party of China (CPC)

Academic Title: Associate Professor, Master’s Supervisor

Affiliated Institution: Institute of Computer Application Technology

Office Address: Room 513, Feiyun Building

Educational Background

2010.09-2014.06 Bachelor of Engineering in Computer Science and Technology, School of Information Science and Engineering, Lanzhou University

2014.09-2017.06 Master of Engineering in Software Engineering, School of Information Science and Engineering, Lanzhou University

2017.09-2021.06 Doctor of Engineering in Computer Application Technology, School of Information Science and Engineering, Lanzhou University

Professional Experience

2021.08-2023.09 Postdoctoral Fellow, School of Medical Technology, Beijing Institute of Technology

2023.10-Present Associate Professor, School of Information Science and Engineering, Lanzhou University

Research Interests

1. Diagnosis and Non-pharmacological Intervention of Cerebral Functional Diseases
2. Electrophysiological Signal Analysis
3. Brain Functional Network Analysis

Projects:

1. Science and Technology Innovation 2030 – Major Project of “Brain Science and Brain-like Intelligence Research”: Prospective Clinical Cohort Study on Depression (Grant No. 2021ZD02006001), Participant
2. National Major Scientific Research Instrument Development Project: Multimodal Physiological Information Asynchronous Analysis System for Neural Mechanisms of Emotional Function Abnormalities (Grant No. 62227807), Participant

Publications (Papers and Books)

More than 20 SCI/EI-indexed papers published. Major SCI/EI papers in the past five years are as follows (# Equal First Author, * Corresponding Author):

Journal papers

1.Sun, S., Chen, H., Luo, G., Yan, C., Dong, Q., Shao, X., Li, X., & Hu, B. (2023). Clustering-Fusion Feature Selection Method in Identifying Major Depressive Disorder Based on Resting State EEG Signals.IEEE Journal of Biomedical and Health Informatics.

2.Sun, S., Qu, S., Yan, C., Luo, G., Liu, X., Dong, Q., & Li, X. (2023). A Study of Major Depressive Disorder Based on Resting-State Multilayer EEG Function Network.IEEE Transactions on Computational Social Systems.

3.Sun, S.#, Liu, L.#, Shao, X., Yan, C., Li, X., & Hu, B. (2022). Abnormal Brain Topological Structure of Mild Depression During Visual Search Processing Based on EEG Signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 1705-1715.

4.Sun, S., Yang, P., Chen, H., Shao, X., Ji, S., Li, X., … & Hu, B. (2022). Electroconvulsive Therapy-Induced Changes in Functional Brain Network of Major Depressive Disorder Patients: A Longitudinal Resting-State Electroencephalography Study. Frontiers in Human Neuroscience, 16.

5.Chen, H.#, Sun, S.#, Li, J., Yu, R., Li, N., Li, X., & Hu, B. (2021). Personal-zscore: Eliminating individual difference for eeg-based cross-subject emotion recognition. IEEE Transactions on Affective Computing.

6.Sun, S., Li, X., Zhu, J., Wang, Y., La, R., Zhang, X., … & Hu, B. (2019). Graph theory analysis of functional connectivity in major depression disorder with high-density resting state EEG data. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(3), 429-439.

7.Shao, X., Sun, S., Li, J., Kong, W., Zhu, J., Li, X., & Hu, B. (2021). 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, 29, 1546-1556.

8.Liu, W., Dong, Q., Sun, S., Shen, J., Qian, K., & Hu, B. (2023). Risk Prediction of Alzheimer’s Disease Conversion in Mild Cognitive Impaired Population based on Brain Age Estimation. IEEE Transactions on Neural Systems and Rehabilitation Engineering.

9.Shao, X., Kong, W., Sun, S., Li, N., Li, X., & Hu, B. (2023). Analysis of functional connectivity in depression based on a weighted hyper-network method. Journal of Neural Engineering.

10.Cai, H., Yuan, Z., Gao, Y., Sun, S., Li, N., Tian, F., … & Hu, B. (2022). A multi-modal open dataset for mental-disorder analysis. Scientific Data, 9(1), 178.

11.Shao, X., Yan, D., Kong, W., Sun, S., Liao, M., Ou, W., … & Hu, B. (2023). Brain function changes reveal rapid antidepressant effects Of nitrous oxide for treatment-resistant depression: Evidence from task-state EEG. Psychiatry Research, 322, 115072.

12.Li, X., Zhang, X., Zhu, J., Mao, W., Sun, S., Wang, Z., … & Hu, B. (2019). Depression recognition using machine learning methods with different feature generation strategies. Artificial intelligence in medicine, 99, 101696.

13.Li, X., La, R., Wang, Y., Niu, J., Zeng, S., Sun, S., & Zhu, J. (2019). EEG-based mild depression recognition using convolutional neural network. Medical & biological engineering & computing, 57, 1341-1352.

Conference papers:

1.Sun, S., Yan, C., Lyu, J., Xin, Y., Zheng, J., Yu, Z., & Hu, B. (2022, December). EEG Based Depression Recognition by Employing Static and Dynamic Network Metrics. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1740-1744). IEEE. (CCF B)

2.Sun, S., Chen, H., Shao, X., Liu, L., Li, X., & Hu, B. (2020, December). EEG based depression recognition by combining functional brain network and traditional biomarkers. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2074-2081). IEEE. (CCF B)

3.Luo, G., Sun, S.*, Qian, K.*, Hu, B.*, Schuller, B., & Yamamoto, Y. (2023, July)., How does Music Affect Your Brain? A Pilot Study on EEG and Music Features for Automatic Analysis. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

4.Zhang, Y., Gong, T., Sun, S., Li, J., Zhu, J., & Li, X. (2020, December). A functional network study of patients with mild depression based on source location. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1827-1834). IEEE. (CCF B)

5.Mao, W., Zhu, J., Li, X., Zhang, X., & Sun, S. (2018). Resting state eeg based depression recognition research using deep learning method. In Brain Informatics: International Conference, BI 2018, Arlington, TX, USA, December 7–9, 2018, Proceedings 11 (pp. 329-338). Springer International Publishing.

Email: sst@lzu.edu.cn