Algorithms to process brain activity over networks for clinical applications

This exciting inter-disciplinary PhD project will develop mathematical and computational algorithms to analyse brain activity and brain networks with the objective to help monitor neurological conditions.

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 novel information from brain activity recorded in electroencephalogram (EEG) signals. To this end, we will focus on considering 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 assessment of 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.

Further Information: 

Closing Date: 

Wednesday, January 15, 2020

Principal Supervisor: 


Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.

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 but it is not an essential requirement.

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

Previous knowledge in areas related to signal processing (e.g., Fourier, time series analysis) or graph theory (e.g., matrix algebra, networks, etc.) would be expected.


Further information and other funding options.

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere

Informal Enquiries: