Epileptic seizure prediction and the dynamics of the electrical fields of the brain

Aim

The central aim of this project focuses on seizure prediction to facilitate the development of such a therapy. It is envisaged that a neural prosthesis will modulate and control seizures for the millions of people worldwide with epilepsies that are not reduced by medicine and unsuitable for surgery. On a broader level this research is an investigation into general dynamics of the electrical fields produced by the brain.

Description

The problem of intractable epileptic seizures is significant. The challenge of developing new treatment strategies has brought together clinicians, neuroscientists, mathematicians and engineers in an effort to gain a greater understanding of brain activity associated with epileptic events. This growing wave of research follows the success of devices such as the vagal-nerve stimulator and deep brain stimulator for the treatment of Parkinson’s disease. It is envisaged that if epileptic seizures could be predicted or anticipated, it would be possible to deliver focal therapy for seizure abatement.

A direction complimenting seizure prediction is an investigation into the direct brain computer interface (BCI). In general, a BCI takes a signal from the brain, classifies patterns corresponding to particular activity (or thought) and transmits the user’s intent down a communication channel. The goal of the BCI is to detect normal activity where seizure prediction is detecting abnormal activity. From a purely scientific perspective, BCI research aims to add to the knowledge of brain behaviour and learning. Human learning can be studied by examining how a subject improves in performance when using a BCI. This will also lead to development of machine learning, which can be studied by developing a classification technique to extract relevant brain patterns.

People

Mr Dean Freestone	Prof Tony Burkitt
Prof Iven Mareels	Prof Mark Cook

Funding

Research supported by the ARC Linkage Project “Prediction of Epilepsy Seizure Onset Using Nonlinear Analysis of EEG Recordings” (LP0560684).