The European Epilepsy Database

The European Epilepsy Database

The Epilepsy Database of the University Medical Center Freiburg is a major resource for EEG data from epilepsy patients. The Epilepsy Center has created the world’s most comprehensive public EEG database within the framework of the EU project EPILEPSIAE (Grant 211713), comprising 275 annotated datasets from over 250 patients.
Each dataset includes at least 96 hours of continuous EEG recordings at sampling rates of up to 2500 Hz, complemented by clinical patient information and MRI data — totaling more than 45,000 hours and 1,800 seizures.

The database was established as the European Epilepsy Database through collaboration between centers in Freiburg, Coimbra, and Paris, and has been accessible via the Epilepsy Center since 2012. The center uses the data for research on seizure prediction, neurostimulation, and therapy monitoring, for example in studies on brain excitability without stimulation. Recent projects, such as long-term EEG implant studies (2025), build upon this foundation.

Do you need EEG data for research or development purposes? Here is our offer for you — feel free to contact us directly for more information and tailored solutions: matthias.duempelmann@uniklinik-freiburg.de

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Die EPILEPSIAE Project Database

Many technological applications in the fields of neurology and neuroscience rely on the analysis of EEG data. Until recently, public resources for EEG recordings have been available only to a limited extent. Therefore, within the framework of the EPILEPSIAE project, an extensive database of long-term intracranial and surface EEG recordings was compiled.

This epilepsy database is by far the largest and most comprehensive database of human surface and intracranial EEG data. It is suitable for a wide range of applications, including time-series analyses of brain activity, the development of machine learning algorithms, research on seizure prediction, and clinical studies.

Comparison of the European Epilepsy Database with Other Databases

Selected Publications Using the EPILEPSIAE Database

1. Reisinger P, Larochelle J, Abkai C, Kalousios S, Zabler N, Dümpelmann M, Schulze-Bonhage A, Woias P, Comella L. The impact of EEG preprocessing parameters on ultra-low-power seizure detection. Epilepsia. 2025 Oct;66(10):3895-3906. doi: 10.1111/epi.18521.
2. Guendelman M, Vekslar R, Shriki O. A New Perspective in Epileptic Seizure Classification: Applying the Taxonomy of Seizure Dynamotypes to Noninvasive EEG and Examining Dynamical Changes across Sleep Stages. eNeuro. 2025 Jan 16;12(1):ENEURO.0157-24.2024. doi: 10.1523/ENEURO.0157-24.2024.
3. Oliveira A, Pinto MF, Lopes F, Leal A, Teixeira CA. Classifier Combination Supported by the Sleep-Wake Cycle Improves EEG Seizure Prediction Performance. IEEE Trans Biomed Eng. 2024 Aug;71(8):2341-2351. doi: 10.1109/TBME.2024.3368304.
4. Sopic D, Teijeiro T, Atienza D, Aminifar A, Ryvlin P. Personalized seizure signature: An interpretable approach to false alarm reduction for long-term epileptic seizure detection. Epilepsia. 2023 Dec;64 Suppl 4:S23-S33. doi: 10.1111/epi.17176.
5. Baghersalimi S, Teijeiro T, Atienza D, Aminifar A. Personalized Real-Time Federated Learning for Epileptic Seizure Detection. IEEE J Biomed Health Inform. 2022 Feb;26(2):898-909. doi: 10.1109/JBHI.2021.3096127.
6. Liu T, Truong ND, Nikpour A, Zhou L, Kavehei O. Epileptic Seizure Classification With Symmetric and Hybrid Bilinear Models. IEEE J Biomed Health Inform. 2020 Oct;24(10):2844-2851. doi: 10.1109/JBHI.2020.2984128.
7. Meisel C, Schulze-Bonhage A, Freestone D, Cook MJ, Achermann P, Plenz D. Intrinsic excitability measures track antiepileptic drug action and uncover increasing/decreasing excitability over the wake/sleep cycle. Proc Natl Acad Sci U S A. 2015 Nov 24;112(47):14694-9. doi: 10.1073/pnas.1513716112.

8. Donos C, Dümpelmann M, Schulze-Bonhage A. Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification. Int J Neural Syst. 2015 Aug;25(5):1550023. doi: 10.1142/S0129065715500239.
9. Alvarado-Rojas C, Valderrama M, Fouad-Ahmed A, Feldwisch-Drentrup H, Ihle M, Teixeira CA, Sales F, Schulze-Bonhage A, Adam C, Dourado A, Charpier S, Navarro V, Le Van Quyen M. Slow modulations of high-frequency activity (40-140-Hz) discriminate preictal changes in human focal epilepsy. Sci Rep. 2014 Apr 1;4:4545. doi: 10.1038/srep04545.
10. Klatt J, Feldwisch-Drentrup H, Ihle M, Navarro V, Neufang M, Teixeira C, Adam C, Valderrama M, Alvarado-Rojas C, Witon A, Le Van Quyen M, Sales F, Dourado A, Timmer J, Schulze-Bonhage A, Schelter B. The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients. Epilepsia. 2012 Sep;53(9):1669-76. doi: 10.1111/j.1528-1167.2012.03564.x.

Note: This list contains selected publications. For a complete list or if you have published work using the EPILEPSIAE database, please contact:
matthias.duempelmann@uniklinik-freiburg.de