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Exploring Algorithms for Early Seizure Detection

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Non-EEG Seizure.jpg


Epilepsy is a severe and prevalent chronic neurological disorder affecting 1–2% of the world’s population. One third of epilepsy patients continue to suffer from seizures despite best possible pharmacological treatment. For these patients with so-called drug-resistant epilepsy, various algorithms based on intracranial electroencephalography (iEEG) recording are proposed to detect the onset of seizures. Among them are algorithms based on brain-inspired hyperdimensional (HD) computing available at The main goal of this project is to enhance current HD algorithms, or propose new ones, to especially reduce the delay of seizure onset detection on long-term iEEG dataset, ultimately pushing towards early seizure detection.

Status: Completed

Anna Summerauer

Supervision: Michael Hersche, Abbas Rahimi


  • Machine Learning
  • Python Programming
  • Cuda Programming


40% Theory
60% Programming


Luca Benini

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