Real-Time, Personalised Passive Brain-Computer Interface for Enhanced Cognition and Neurorehabilitation

Summary

A Brain-Computer Interface (BCI) decodes brain activity patterns and transforms them into actionable commands, with applications ranging from enhancing brain functions to treating neurological dysfunctions. This PhD project delves into the promising field of passive BCI which can monitor more nuanced and real-life mental states, such as mental workload, fatigue, attention, mood, and arousal.

The project’s goal is to develop an innovative, real-time, and adaptive monitoring system that integrates seamlessly with using magnetoencephalography (MEG) and electroencephalography (EEG) based BCI frameworks. This personalised system will monitor cognitive and emotional states alongside physiological and behavioural metrics, enhancing outcomes for cognitive tasks like decision-making and motor rehabilitation.

The proposed system addresses a critical gap in BCI technology adoption by focusing on passive, user-adaptive systems that are intuitive and responsive to individual needs. By integrating physiological, affective, and behavioural signals, the project will create a trust-enhancing, user-centric BCI framework, boosting both effectiveness and user experience.

The project will leverage the pioneering Cognitive Neuroscience and Neurotechnology team's expertise, encompassing BCI, neuroimaging, neuroscience, neurorehabilitation, machine learning, and cognitive computational modelling. It will involve collecting a rich set of physiological and behavioural data. Cutting-edge machine learning and computational modelling will be employed to handle the complex data, identify critical measures, enhance model interpretability, and reduce user training time while ensuring comfort and engagement. Neural functional connectivity will improve detection of brain state changes, offering enhanced explainability, and personalised support. By recognising when mental fatigue or emotional states impact cognitive or motor function, the system will dynamically adjust task pacing and intensity, optimising user performance.

The successful PhD candidate will work at the forefront of neurotechnology, developing skills in neural and physiological signal processing, machine learning, cognitive computational modelling, and high-performance computing. This project will prepare impactful careers in data science, artificial intelligence, neurotechnology, biotechnology, and beyond.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain
• A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)