Available Projects
The project list below helps self-funded and externally sponsored applicants (scholarship) connect with a prospective supervisor.
Applications are welcome from those who can support themselves or have funding already arranged. Such applications will go through the same level of academic assessment. Please see the fee arrangementsLink opens in a new window for PGR students.
EEG signal analysis in the home / away from the clinic: on the development of robust and efficient data analysis techniques
Electroencephalography (EEG) has significant potential for monitoring neurological health outside clinical environments, but real-world recordings are often affected by noise, artefacts, and variable usage conditions. This project will develop robust and efficient signal processing and AI-based analysis techniques for home-based EEG monitoring, enabling reliable assessment of brain activity and supporting future applications in neurological diagnosis, healthcare, and personalised medicine.
Primary supervisor: Christopher James - c.james@warwick.ac.uk
Project detail:
Electroencephalography (EEG) is a widely used non-invasive technique for measuring brain activity and plays an important role in the diagnosis and management of neurological disorders. Advances in wearable and portable EEG technologies are creating new opportunities to move brain monitoring from specialist clinical settings into homes and everyday environments. Such systems have the potential to support long-term monitoring, capture more representative measures of brain activity, and enable earlier detection of neurological changes. However, EEG recordings collected outside controlled clinical environments present significant technical challenges, including motion artefacts, environmental noise, inconsistent sensor placement, and variations in user behaviour.
This PhD project will focus on the development of robust and efficient data analysis techniques for EEG recordings acquired in real-world settings. The research will investigate novel signal processing, machine learning, and artificial intelligence approaches capable of extracting meaningful information from noisy and highly variable EEG data. The project will explore methods for artefact detection and removal, feature extraction, automated pattern recognition, and adaptive modelling that can operate reliably in unconstrained environments.
A key objective will be to improve the quality, reliability, and interpretability of EEG-based metrics collected outside traditional healthcare settings. The successful candidate will examine how advanced analytical approaches can support the accurate assessment of neurological state and function, while maintaining computational efficiency suitable for portable and potentially real-time applications. Opportunities may also exist to integrate EEG data with other physiological measurements from wearable technologies, creating richer representations of health status and behaviour.
The project sits at the intersection of biomedical engineering, signal processing, neuroscience, and artificial intelligence. The student will gain expertise in advanced data analytics, physiological signal processing, machine learning, and digital health technologies, while working with researchers across multiple disciplines. Access to modern EEG systems and real-world datasets will provide opportunities to develop solutions with clear translational potential.
The outcomes of this research could contribute to the next generation of remote neurological monitoring technologies, supporting applications such as epilepsy management, cognitive health assessment, sleep monitoring, neurorehabilitation, and early detection of neurological disorders. By enabling reliable EEG analysis beyond the clinic, this work aims to help transform neurological healthcare towards more continuous, accessible, and patient-centred models of care.
How to apply for admission: www.warwick.ac.uk/pgrengineering
The University of Warwick provides an inclusive working and learning environment, recognising and respecting every individual’s differences. We welcome applications from individuals who identify with any of the protected characteristics defined by the Equality Act 2010.