If you are interested in using MODMA dataset, please take the following steps:
1. You will have to register your own account on MODMA website using your organization email. Before you sign in for the first time, you have to check your email and activate your account.
2. All current publicly available MODMA datasets are listed below with descriptions. You may select the ones you wish to download by check the checkboxes on the left. Then click the “Next” button.
3. On the next page, you will have to download, print, sign and scan an EULA (End User License Agreement) and upload it through the link provided. Then click the "Submit" button.
4. Upon receipt of the EULA, the access of selected dataset files will be granted by dataset administrators. Depend on the workload of the dataset administrators, this could take a couple of days.
5. You may log in your personal page to check whether the accesses are granted. Once you do, you may download the requested files through your MODMA website "HowToAccess" section.


* The MODMA is a multi-modal dataset, the subject id across different sub-dataset is unified.

EEG_128channels_ERP_lanzhou_2015
  • 128-channel Event-Related Potential recordings
  • 24 Major Depressive Disorder subjects and 29 Healthy Control subjects
  • Age range : 16-52 years old
  • Includes: 1) demographic data, 2) psychological assessments
  • File size: 4.8G (for extracting of large file, you may use new verion of pupular tools like winrar, 7z. )
  • Md5sum: ac7b82ecf62de5c8783eaa0376674e9b

  • Latest publication based on this dataset:

    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 Download

  • Related references:

    Li, X., Li, J., Hu, B., Zhu, J., Zhang, X., Wei, L., ... & Zhang, L. (2018). Attentional bias in MDD: ERP components analysis and classification using a dot-probe task. Computer methods and programs in biomedicine, 164, 169-179. Download

    Hu, B., Rao, J., Li, X., Cao, T., Li, J., Majoe, D., & Gutknecht, J. (2017). Emotion regulating attentional control abnormalities in major depressive disorder: an event-related potential study. Scientific reports, 7(1), 1-21. Download

  • Dot probe:

    Download

  • EEG_128channels_resting_lanzhou_2015
  • 128-channel resting-state recordings
  • 24 Major Depressive Disorder subjects and 29 Healthy Control subjects
  • Age range : 16-52 years old
  • Includes: 1) demographic data, 2) psychological assessments
  • File size: 2.2G
  • Md5sum: 7f5ea8c89c550443dc740c5c9e9d3867

  • Latest publication based on this dataset:

    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 Download

    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.Download

  • Related references:

    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. Download

    Peng, H., Xia, C., Wang, Z., Zhu, J., Zhang, X., Sun, S., ... & Li, X. (2019). Multivariate Pattern Analysis of EEG-Based Functional Connectivity: A Study on the Identification of Depression. IEEE Access, 7, 92630-92641. Download

    Li, X., Jing, Z., Hu, B., Zhu, J., Zhong, N., Li, M., ... & Majoe, D. (2017). A resting-state brain functional network study in MDD based on minimum spanning tree analysis and the hierarchical clustering. Complexity, 2017. Download

  • Resting state:

    Download

  • EEG_3channels_resting_lanzhou_2015
  • 3-channel resting-state recordings
  • 26 Major Depressive Disorder subjects and 29 Healthy Control subjects
  • Age range : 16-56 years old
  • Includes: 1) demographic data, 2) psychological assessments
  • File size: 142M
  • Md5sum: b4782823a5f4583de12c6334ebcd67c5

  • Latest publication based on this dataset:

    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 Download

    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 Download

  • audio_lanzhou_2015
  • 23 Major Depressive Disorder subjects and 29 Healthy Control subjects
  • Age range = 18-52 years old
  • Includes: 1) demographic data, 2) psychological assessments
  • File size: 2.5G
  • Md5sum:5550552006ec4ae5a89ecb86b7a147ac

  • Latest publication based on this dataset:

    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 Download

    Liu, Z., Wang D., Zhang L., & Hu, B. (2020) A Novel Decision Tree for Depression Recognition in Speech. arXiv preprint arXiv:2002.12759. Download

  • Next

    Please download, print, sign and scan an EULA (End User License Agreement) and upload it. Then click the "Submit" button.

    Download MODMA Dataset EULA (End User License Agreement) Template