Publications
1. Dataset description reference
Cai, H., Gao, Y., Sun, S., Li, N., Tian, F., Xiao, H., Li, J., Yang, Z., Li, X., Zhao, Q., Liu, Z., Yao, Z., Yang, M., Peng, H., Zhu, J., Zhang, X., Hu, X., & Hu, B. (2020). MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis. arXiv preprint arXiv:2002.09283
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2. Latest publications based on the individual dataset
2.1 EEG
2.1.1 Dataset : eeg_128channels_ERP_lanzhou_2015
2.1.2 Dataset : eeg_128channels_resting_lanzhou_2015
Sun, S., Li, J., Chen, H., Gong, T., Li, X., & Hu, B. (2020). A study of resting-state EEG biomarkers for depression recognition. arXiv preprint arXiv:2002.11039.
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2.1.3 Dataset : eeg_3channels_resting_lanzhou_2015
Shi, Q., Liu, A., Chen, R., Shen, J., Zhao, Q., & Hu, B. (2020). Depression Detection using Resting State Three-channel EEG Signal. arXiv preprint arXiv:2002.09175
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2.2 Audio
2.2.1 Dataset : audio_lanzhou_2015
Liu, Z., Wang D., Zhang L., & Hu, B. (2020) A Novel Decision Tree for Depression Recognition in Speech. arXiv preprint arXiv:2002.12759.
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3. Related references
Yang, M., Wu, Y., Tao, Y., Hu, X., & Hu, B. (2023). Trial Selection Tensor Canonical Correlation Analysis (TSTCCA) for Depression Recognition with Facial Expression and Pupil Diameter. IEEE Journal of Biomedical and Health Informatics. doi: 10.1109/JBHI.2023.3322271. Download Cite
Yang, M., Gao, Y., Tang, L., Hou, J., & Hu, B. (2024). Wearable Eye-Tracking System for Synchronized Multimodal Data Acquisition. IEEE Transactions on Circuits and Systems for Video Technology, 34(6), 5146-5159. doi: 10.1109/TCSVT.2023.3332814. Download Cite
Tao, Y., Yang, M., Li, H., Wu, Y., & Hu, B. (2024). DepMSTAT: Multimodal Spatio-Temporal Attentional Transformer for Depression Detection. IEEE Transactions on Knowledge and Data Engineering, 36(7), 2956-2966. doi: 10.1109/TKDE.2024.3350071. Download Cite
Yang, M., Weng, Z., Zhang, Y., Tao, Y., & Hu, B. (2023). Three-Stream Convolutional Neural Network for Depression Detection With Ocular Imaging. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 4921-4930. doi: 10.1109/TNSRE.2023.3339518. Download Cite