Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge

1.    The contest data from the ‘Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge’ that can be downloaded here, and any derivatives of the data, can only be used for non-commercial purposes. Any use of the data, or part of the data, including software development, algorithm testing, or any other use, must be for non-commercial purposes. 

2.    The data may be used for academic purposes such as student assignments or projects and research purposes such as journal and conference publications, under the constraint that use of the contest data in journal and conference publications requires including Dr Levin Kuhlmann from the University of Melbourne and Swinburne University of Technology as a co-author for preparing the data and benchmarking your algorithm against other algorithms in the ecosystem. Noting this requirement, Dr Kuhlmann reserves the right to decide to not be part of any such publication. Class labels for the training and public test sets from the contest are provided. To submit your complete test set predictions and obtain Area Under the Curve (AUC) performance scores for your algorithm for the private test set please contact Dr Levin Kuhlmann at

Department of Medicine – St. Vincent’s Hospital Melbourne
St Vincent’s Hospital Melbourne
Clinical Sciences Building,
Level 4 / 29 Regent Street,
Fitzroy VIC 3065, Australia

3.    Use of the contest data and/or top algorithms in journal, conference or other publications, and/or source code requires attribution of the original source through citation of the following paper:
Kuhlmann, L., Karoly, P., Freestone, D.R., Brinkmann, B.H., Temko, A., Barachant, A., Li, F., Titericz Jr., G., Lang, B.W., Lavery, D., Roman, K., Broadhead, D., Dobson, S., Jones, G., Tang, Q., Ivanenko, I., Panichev, O., Proix, T., Náhlík, M., Grunberg, D.B., Reuben, C., Worrell, G., Litt, B., Liley, D.T.J., Grayden, D.B., & Cook, M.J. (2018) Crowd-Sourcing Reproducible Seizure Prediction with Long-Term Human Intracranial EEG, Brain, awy210,
4.    The algorithms of the top performing teams from the ‘Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge’ that can be downloaded under the auspices of are publicly available as open source.


Melbourne Seizure Prediction Trial Seizure Data

This data is licensed under a Creative Commons license with conditions on ATTRIBUTION, NON-COMMERCIAL use and SHARE-ALIKE


  • Data use is for private individual/research/teaching training and no other purpose

  • Commercial entities may not use this data for any reason

  • This data may not be used for patent applications, licensed software, IP or other for-profit use


Data should be cited as the Melbourne University NeuroVista Seizure Prediction Data and reference:

Cook, Mark J., et al. "Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study." The Lancet Neurology 12.6 (2013): 563-571.


  • Derivative works must be distributed under a Creative Commons (or equivalent) license.

  • Derivative works may include (but not limited to) neural networks, classifiers, mathematical models, computational models, networks, figures that are derived substantially from the data

For more information refer to:

Karoly, Philippa J., et al. "Seizure pathways: A model-based investigation." PLoS Computational Biology (2018):