top of page

Chang Lei

2022-Current Ph.D. Candidate, Tsinghua University

2022 Master of Engineering, Lanzhou University

2019 Bachelor of Engineering, Zhengzhou University

Research experiences:

  1. Research on potential depression risk early warning theory and biosensing key technology based on biological and psychological multimodal information.

  2. Development of a comprehensive intelligent experimental system for the research of "neural mechanism-brain-like computing model".

  3. Detection and prediction of epileptic seizures based on EEG signals.



  1.  Lei, C., Qu, D., Liu, K., & Chen, R. (2023). Ecological momentary  assessment and machine learning for predicting suicidal ideation among sexual and gender minority individuals. JAMA network open, 6(9), e2333164-e2333164.

  2. Lei, C., Zheng, S., Zhang, X., Wang, D., Wu, H., Peng, H., & Hu, B. (2021). Epileptic seizure detection in EEG signals using discriminative Stein kernel-based sparse representation. IEEE Transactions on Instrumentation and Measurement, 71, 1-15.

  3. Peng, H.#, Lei, C.#, Zheng, S., Zhao, C., Wu, C., Sun, J., & Hu, B. (2021). Automatic epileptic seizure detection via Stein kernel-based sparse representation. Computers in Biology and Medicine, 132, 104338.

  4. Lei, C., Wang, D., Chao, J., Zhang, X., Zheng, S., Wu, H., & Peng, H. (2021, December). Seizure onset detection using common spatial pattern and discriminative log-Euclidean kernel-based Gaussian process. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2259-2265). IEEE.

  5. Wang, D., Lei, C., Zhang, X., Wu, H., Zheng, S., Chao, J., & Peng, H. (2021, December). Identification of depression with a semi-supervised GCN based on EEG data. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2338-2345). IEEE.

  6. Zheng, S., Lei, C., Wang, T., Wu, C., Sun, J., & Peng, H. (2020, December). Feature-level fusion for depression recognition based on fNIRS data. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2906-2913). IEEE.

  7. Xu, J., Zhang, Y., Lei, C., Sun, P., Chen, R., & Yuan, T. (2023). Using machine learning to identify factors related to nitrous oxide (laughing gas) relapse among adolescents. General Psychiatry, 36(2).

  8. Chao, J., Zheng, S., Lei, C., Peng, H., & Hu, B. (2022). Exploratory cross-frequency coupling and scaling analysis of neuronal oscillations stimulated by emotional images: An evidence from EEG. IEEE Transactions on Cognitive and Developmental Systems.

bottom of page