April 16:  Piotr Mirowski, CIMS

Towards seizure prediction from EEG
  

  Could we predict seizures? Clinical case studies reported that some epileptic patients have premonitory symptoms minutes to hours before the seizure onset, and recent research has measured corresponding electrophysiological changes in the brain activity.

In this talk, I will present our patent-pending algorithm for epileptic seizure prediction, developed in collaboration with Yann LeCun and the NYU Medical Center, and bridging a long-standing neurophysiological research problem with machine learning algorithms. Our software extracts patterns of synchronization between intracranial EEG, and feeds them into an L1-regularized convolutional network. Evaluated on datasets of patients with medically-intractable epilepsy, our method significantly outperformed previous research in the field.

There is still room for improvement before we could translate our methodology to human neuro-prosthetics. I will therefore briefly mention my PhD research on time series modeling, using state-space models and gradient-based approximations to the EM algorithm, and explain how I am working on improving our current seizure prediction system.