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Data-driven processing of brain activity over networks

Project Description

This exciting inter-disciplinary PhD project will develop computational and mathematical methods to analyse brain activity and networks to help monitor some of the most important neurological diseases today.

The successful applicant will do research in an inter-disciplinary and emerging area at the interface of signal processing and network theory, in collaboration with clinical collaborators and benefitting from datasets acquired in real-world settings. Applications of signal processing and network theory are expanding nowadays, and this PhD provides an excellent opportunity to be trained in these areas.

We will create data-driven signal processing methods for the analysis of temporal signals recorded at different, but related, spatial locations. We expect that these techniques will enable the extraction of new information from the electroencephalogram (EEG) activity beyond that currently available. To this end, we will consider two key aspects of brain activity simultaneously: its temporal dynamics and connectivity.

The importance of brain connectivity has recently been recognised. Different parts of the brain need to interact with each other in a coordinated way for a healthy function. In the EEG, this is assessed through the analysis of functional connectivity: statistical dependencies between multivariate time-varying recordings of brain activity acquired at distinct locations. Network science is then used to study the connectivity patterns from a system’s perspective by decomposing them into a set of elements and relationships between them. However, despite the relevance of these approaches, their actual practical use in the clinic is still limited. This is because current methods tend to disregard the dynamical nature of brain activity that makes connectivity patterns evolve rapidly in time. Thus, we need new signal processing and network analyses to better characterize the dynamical and multifaceted nature of brain activity.

To do so, we will build on recent developments by our group and others in the areas of graph theory, graph signal processing, and graph variate analysis. We expect that this PhD will lead to developments that could be transferred to the monitoring of disease in the clinic.

Enthusiastic and self-motivated candidates are sought with, at least, an Honours degree at 2:1 or above (or International equivalent) in electronic engineering, computer science, mathematics or cognate disciplines. An MSc qualification will be advantageous.

The candidate is expected to have good programming and analytical skills.

Previous experience in areas related to graph theory (e.g., networks, algebra, etc.) or signal processing (e.g., time series analysis) would be beneficial but it is not necessary.

Apply here:

Funding Notes

Applications are welcomed from self-funded students, or students who are applying for scholarships.

How good is research at University of Edinburgh in General Engineering?
(joint submission with Heriot-Watt University)

FTE Category A staff submitted: 91.80

Research output data provided by the Research Excellence Framework (REF)

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