While seizure detection is necessary to activate an electrical stimulation device at the right time to abort a seizure, seizure prediction is also very important, and has a variety of benefits. First, it can be used to generate an electrical stimulus just before seizure onset in order to prevent a seizure from occurring. This is more useful than seizure detection, which relies on detecting the onset of a seizure, and thus there is a delay between seizure onset and abortion of a seizure through electrical stimulation. Second, anti-convulsant drugs can be slow acting (on the time scale of minutes), thus if a seizure prediction method could predict an event minutes in advance, it could activate a drug delivery system to diffuse a drug into the seizure region of the brain and prevent a seizure from occurring. Third, if a seizure can be predicted minutes in advance the patient could be warned and they can move themselves to safety. Fourth, patients often undergo brain scans to determine the focus of epileptic seizures for the purposes of surgical removal of the epileptic region, these scans require injection of a radioactively labeled marker used to indicate the focus. If a doctor could be warned about a seizure a few minutes in advance they could inject the marker at the right time to determine the seizure focus more accurately.

Current seizure prediction algorithms analyse the electrical signals recorded from the brain using methods similar to seizure detection. These algorithms draw on techniques from signal processing, non-linear dynamical systems theory, and machine learning. Instead of providing a seizure onset signal within milliseconds after the actual seizure onset, as is done by detection algorithms, prediction algorithms try to predict seizures on time scales of seconds, minutes, and even hours before seizures, depending on the algorithm.

Our group is seeking students to both develop new seizure prediction algorithms, and implement other methods from the literature, that can be compared with algorithms that will be developed by our group. We have a primary interest in methods based on signal processing and non-linear dynamical systems theory, since they are usually simpler and easier to implement in an implantable device. However, we are also interested in looking at machine learning based methods in order to determine the performance of different seizure prediction algorithms. In the long term, our group aims to use seizure prediction algorithms as part of an implanted device used to prevent seizures.

Despite years of research into epilepsy, little is actually known about the underlying causes of epilepsy. Largely, because it can occur in several parts of the brain, and involves large populations of neurons (brain cells) that interact with one another in a complex way. In an attempt to understand the underlying causes of epilepsy we are forming a collaboration to try to develop neural models of epilepsy at various scales of brain structure.

At a low level, neurons connect to other neurons via electrochemical connections called synapses. These synapses involve ion channels that are activated by neurotransmitters (i.e. a chemical messenger), and transfer electrical charge from one cell to another. Thus providing the basis of electrochemical signaling between cells. Certain types of ion channels are genetically related to epileptics. At a larger scale, networks of neurons that become overly excited are thought to be directly associated with epileptic seizures. Depending on the interests of the student we would like to begin developing neural models of epilepsy at different scales: (1) ion channels/synapses, (2) single neurons, or (3) networks of neurons. At each of these levels we are interested in modeling the changes in dynamics associated with changes in ion channel structure and properties. These changes in ion channels are related to genetic mutations that are associated with certain types of epilepsy

Performing invasive experiments in humans with epilepsy presents us with various ethical dilemmas. Thus scientists have developed animal models of epilepsy as an alternative. In these models, the genetic, electrochemical, and/or structural make-up of the normal animal brain is altered such that 'epileptic-like' seizures can be generated in the brain. Thus animal models provide a means of trying to discover the underlying causes of epilepsy. The EEE Department, St. Vincent's Hospital of Melbourne and the Bionic Ear Institute have formed a collaboration to study animal models of epilepsy, as well as develop electrical stimulation or anti-convulsant drug delivery mechanisms that could be used to prevent seizures from occurring.

During physiological experiments in animal models, the responses of brain cells (neurons) will be recorded while seizures are generated in the brain. It is believed that seizures result from populations of neurons which synchronize their activities and become abnormally overactive.

Our group is seeking a student to analyse the responses of populations of neurons recorded during seizures in different parts of an animal brain. This will involve the application of statistical, signal processing, and non-linear dynamical systems theory methods to try and look for relationships, or interactions, between neural responses within specific regions of the brain. The main approach will be to look for synchrony between neural responses using such methods as cross-correlation, phase synchrony or non-linear interdependence. By performing a quantitative analysis of the neural responses we hope to understand the way in which neurons are recruited into a seizure, as well as the sequence of events involved in seizures.

Direct electrical stimulation of the brain has great potential for being the primary means with which to prevent seizures. One way to stimulate the brain is to place a grid of electrodes, in the cranium, on top of the brain surface. Such a grid can be used for the recording of electroencephalographic (EEG) signals (i.e. brain waves), which can be analysed to detect or predict seizures.

To design an electrical stimulation implant for the prevention of seizures, a significant degree of processing will be required to detect/predict seizures and provide the appropriate stimulation sequence. To increase the mobility of patients with implants, we are seeking a student interested in developing a wireless intracranial electrode grid, that can record electrical signals from the brain, as well as provide electrical stimulation, and communicate with an external device. An external device is expected to be necessary for implementing seizure detection/prediction algorithms, as well as for generating electrical stimulation protocols.