PhD Study : ​Acceleration of neurorehabilitation of stroke survivors using FES

Summary

Stroke is a leading cause of disabilities in the UK [1]. Intensive repetitive movement therapy, supported by a therapist, can help recover function - but often function is permanently impaired. Rehabilitation using non-invasive Brain-Computer Interfacing (BCI) that imagines movement (motor imagery) while receiving visual feedback modifies the neuronal activity through progressive practice that may help patients achieve full functional recovery [3, 8]. Functional Electrical Stimulation (FES) intervention is often applied during rehabilitation to directly engage muscles located in the impaired section of the body. FES has shown to be effective for rehabilitation, where muscles in the hand or arm are stimulated with mild electrical currents [4]. Bhattacharyya et al. in [5]-[7] provided conclusive evidence on the enhancement of motor learning across successive sessions among healthy participants when FES was implemented as feedback.

This project aims to design a low-cost BCI-FES solution (in collaboration with NeuroCONCISE Ltd) to provide patient-centered, home-based rehabilitation. The research will include developing an online, adaptive FES based BCI (aFES-BCI) rehabilitation system using novel, state-of-the-art deep learning architecture that are explainable in nature. The aFES-BCI system will then be interfaced with the FlexEEG system provided by NeuroCONCISE Ltd. Finally pilot trials with stroke survivors will be conducted to test the integrated system and measure the intervention outcomes.

The successful PhD candidate will benefit from the wide expertise of the university’s Computational Neuroscience and Machine Learning community and will gain valuable knowledge in machine learning, transfer learning, high-performance computing, mathematics/statistics and neuroscience and develop skills in commercialization of research outputs for licensing and patents. The research experience will open wide opportunities for the student in finding skilled work in the field of data science, machine/deep learning and neuro-technology.

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 comprehensive and articulate personal statement

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

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living