Predicting User Performance for Enhanced Brain-Computer Interfaces
Applications are now CLOSED
Overview
Brain-Computer Interfaces (BCIs) are at the forefront of human-computer interaction, offering a remarkable means for individuals to control external devices and software applications directly with their brain. While BCIs hold immense potential for improving the quality of life for people with disabilities and fostering innovation across various domains, they exhibit significant variability in performance between users, and even session to session. This project seeks to address this challenge by developing predictive models to anticipate BCI user performance, ultimately optimising user experience and guiding the development of more efficient BCI systems.
The project aims to develop a robust predictive model that can anticipate BCI user performance based on a variety of factors. This project's main objectives are:
1. Develop Predictive Models for BCI User Performance: Leveraging machine learning and statistical techniques, this project will create predictive models capable of estimating BCI user performance. A wide array of algorithms, including support vector machines, deep learning models, and regression analysis, will be evaluated for their predictive accuracy. These models will be rigorously trained and tested using collected data and publicly available datasets.
2. Explore User-Specific Factors Impacting BCI Control: The project aims to integrate predictive models into BCI systems, enabling real-time adaptation of system parameters based on user performance. The primary objective is to enhance BCI user control and overall experience.
3. Establish a User-Centric Framework for BCI System Adaptation: This research project will offer valuable insights into creating a user-centric framework for BCI system adaptation, enriching the personalized user experience, and broadening the accessibility of BCI technology.
The research conducted in this project holds the potential to revolutionize the landscape of BCI technology. By making BCIs more accessible and adaptable to a wider user base, the aim is to unlock new possibilities in their development and application. Ultimately, this work contributes to the advancement of neurotechnology, enriches human-computer interaction, and enhances the quality of life for many individuals.
The successful candidate will have the opportunity to gain valuable experience and collaborate with experts across diverse disciplines, including computer science, engineering, psychology, and neuro-computation. The project will encompass the design and execution of experimental research, data analysis, predictive model development, and algorithm creation.
Funding Information
To be eligible for consideration for a Home DfE or EPSRC Studentship (covering tuition fees and maintenance stipend of approx. £19,237 per annum), a candidate must satisfy all the eligibility criteria based on nationality, residency and academic qualifications.
To be classed as a Home student, candidates must meet the following criteria and the associated residency requirements:
• Be a UK National,
or • Have settled status,
or • Have pre-settled status,
or • Have indefinite leave to remain or enter the UK.
Candidates from ROI may also qualify for Home student funding.
Previous PhD study MAY make you ineligible to be considered for funding.
Please note that other terms and conditions also apply.
Please note that any available PhD studentships will be allocated on a competitive basis across a number of projects currently being advertised by the School.
A small number of international awards will be available for allocation across the School. An international award is not guaranteed to be available for this project, and competition across the School for these awards will be highly competitive.
Academic Requirements:
The minimum academic requirement for admission is normally an Upper Second Class Honours degree from a UK or ROI Higher Education provider in a relevant discipline, or an equivalent qualification acceptable to the University.
Project Summary
Dr Alain Desire Bigirimana
Full-time: 3 or 3.5 years