Project Description
Epilepsy is one of the most common serious neurological disorders, affecting approximately 1 percent of the population. For some patients, the seizures can be controlled with medication, but for many they cannot be fully prevented. For these patients, the ability to predict a seizure before it occurs would be of great value.
My project approaches the problem of seizure prediction by employing machine learning techniques. I am working with a dataset of intracranial EEG recordings from 3 patients undergoing pre-surgical monitoring. I intend to explore how different machine learning algorithms can be applied to this problem, with the eventual goal being a system that examines the EEG data and then predicts the time left before seizure onset.