Summary

A Brain-computer Interface (BCI) is a communication and control system that translates mental patterns into actionable commands. Till date, majority of BCI research have focussed on improving its predictive capabilities that involved voluntarily performing a task or evoking different responses to different stimuli [1]. Recently, researchers have taken interest in studying the automatically induced states of the user’s cognition, such as error, mental workload, fatigue, attention focus and arousal while interacting in the surrounding system [2].  Such BCI can monitor the mental status (such as error detection, fatigue, attention focus, etc) of a user or patient while he/she is undergoing neuro-rehabilitation training and take actions to reduce the load on the user or patient. Such measures have the potential to improve the confidence and trust of the users towards BCI technology. Previous research on error monitoring and correction has already been undertaken by members of the research team [3].

This project aims at developing a secondary “Status Monitoring BCI System” to the already existing Magnetoencephalography/Electroencephalography (M/EEG) based neuro-rehabilitation system developed by the research team [4]. The project will develop a generalized predictive model for mental workload, error and fatigue detection over a given period of time while the user undertakes the neuro-rehabilitation training protocol. It includes developing calibration-free machine learning or deep learning models aimed at improving the adaptability of the BCI monitoring system. This may include devising transferable strategies using optimal transport, one-shot or zero-shot learning, etc [5]. This project would also investigate the changing mental states of users using functional connectivity and source localisation techniques and develop a generalised graph based neuronal model from the inference gathered.

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. This experience will open wide opportunities for the student in finding skilled work in the field of data science and machine/deep learning.


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
  • Research proposal of 1500 words detailing aims, objectives, milestones and methodology of the project
  • 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

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,840 (tbc) maintenance grant 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.

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.

Vice-Chancellor’s Research Bursary (VCRB)

Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees and provide the recipient with £8,000 maintenance grant 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.

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.

Vice-Chancellor’s Research Fees Bursary (VCRFB)

Fees only award (PhD fees + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees 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.

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.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £15,840 (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


Recommended reading

[1] Lotte, F, & Roy, R.N. (2019) Brain–Computer Interface Contributions to Neuroergonomics, Editor(s): Hasan Ayaz, Frédéric Dehais, Neuroergonomics, Academic Press, Pages 43-48, https://doi.org/10.1016/B978-0-12-811926-6.00007-5.

[2] Zander, Thorsten & Kothe, Christian & Welke, S. & Roetting, Matthias. (2009). Utilizing Secondary Input from Passive Brain-Computer Interfaces for Enhancing Human-Machine Interaction. Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience. 5638. 759-771. 10.1007/978-3-642-02812-0_86.

[3] Bhattacharyya, S, Konar, A & Tibarewala, DN. Motor Imagery and Error Related Potential Induced Position Control of a Robotic Arm, IEEE/CAA Journal of Automatica Sinica, 4 (4): 639-650.

[4] Chowdhury, A, Raza, H, Meena, YK, Dutta A, & Prasad G (2018) Online Covariate Shift Detection-Based Adaptive Brain–Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 4, pp. 1070-1080, doi: 10.1109/TCDS.2017.2787040.

[5] Lotte F et al (2018) A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update, Journal of Neural Engineering, 15 031005


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