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.
On the development on Blind Source Separation techniques with sparse signals: application to brain signal recordings
Brain recordings often contain mixtures of signals originating from multiple neural and non-neural sources. This project will develop advanced blind source separation techniques for sparse signals, enabling the reliable extraction of meaningful brain activity from complex recordings. By combining signal processing, optimisation, and machine learning, the research aims to improve the analysis of EEG and related neurophysiological data for neuroscience and healthcare applications.
Primary supervisor: Christopher James - c.james@warwick.ac.uk
Project detail:
Brain signal recordings such as electroencephalography (EEG) provide valuable insights into neural function and are widely used in neuroscience research, neurological diagnosis, brain-computer interfaces, and cognitive monitoring. However, the signals recorded at sensors are rarely generated by a single source. Instead, they represent complex mixtures of neural activity, physiological artefacts, environmental interference, and measurement noise. Extracting the underlying brain signals from these mixtures remains one of the most important challenges in biomedical signal processing.
This PhD project will investigate the development of novel blind source separation (BSS) techniques for sparse signals, with a particular focus on applications to brain signal recordings. Blind source separation seeks to recover original signal sources from observed mixtures without detailed prior knowledge of the mixing process. While significant advances have been made in this field, many existing approaches struggle when dealing with real-world neurophysiological recordings characterised by noise, non-stationarity, and complex signal interactions.
The research will explore how sparsity assumptions can be exploited to improve source separation performance and enhance the recovery of meaningful neural information. The project may investigate advanced optimisation methods, sparse signal representations, statistical learning techniques, and machine learning approaches capable of identifying and isolating individual signal components from multichannel recordings. Particular emphasis will be placed on developing methods that are robust, computationally efficient, and applicable to large-scale datasets.
A key objective will be to improve the quality and interpretability of brain signal analysis by separating neural activity from artefacts such as muscle activity, eye movements, motion-related interference, and environmental noise. The resulting techniques could enhance the performance of downstream applications including neurological assessment, cognitive state monitoring, neurotechnology systems, and brain-computer interfaces.
This highly interdisciplinary project combines biomedical engineering, signal processing, applied mathematics, data science, and neuroscience. The successful student will gain expertise in advanced mathematical modelling, optimisation, machine learning, and neurophysiological data analysis, while contributing to the development of next-generation analytical tools for brain monitoring and neurotechnology.
The outcomes of this research have the potential to improve both scientific understanding and practical application of brain signal recordings. By enabling more accurate recovery of underlying neural activity, the project will contribute to more reliable neurophysiological measurements, enhanced diagnostic capabilities, and improved technologies for monitoring and understanding the human brain.
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.