Fully Funded PhD on ‘Co-adaptive Brain Computer Interface’ at The University of Sheffield, Department of Automatic Control and Systems Engineering
University of Sheffield - Automatic Control & Systems Engineering
|Funding for:||UK Students, EU Students|
|Funding amount:||Not specified|
|Placed on:||21st April 2017|
|Closes:||1st May 2017|
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A full-time PhD Studentship is now available in the Department of Automatic Control and Systems Engineering at the University of Sheffield, starting in September 2017. The studentship offers a 4-year funded PhD scholarship open to UK and EU applicants.
The prestigious A*STAR-Sheffield scheme gives the PhD student the unique opportunity to carry out part of their studies in Singapore as part of a partnership agreement with the Agency for Science, Technology and Research (A*STAR). The prospective PhD student will receive half of their training (2 years) in Singapore. Supervision on this project will be provided by Dr Mahnaz Arvaneh and co-supervision of Prof Lyudmila Mihaylova. The project will run in partnership with the Institute for Infocomm Research (I2R) in Singapore with co-supervision of Dr Kai Keng Ang (Department Head and Head of BCI lab) at I2R.
Project description: Brain-computer interface (BCI) allows users to control an external device (e.g. a robotic hand) using their thoughts. Using appropriate sensors and data processing algorithms, a BCI maps patterns of brain activity associated with a volitional thought (e.g. movement imagination) onto signals suitable for communication and control. Thus, BCI can enable users with severe motor disabilities to use their brain signals for communication and control. In the classic approach to BCIs, the participants undertake a calibration session to acquire data to train the BCI system. However, the calibration of a BCI system is a time-consuming and fatiguing process which leaves reduced time for actual interaction. The aim of this project is to research advanced co-adaptive BCI algorithms to facilitate the use of BCI immediately without going through a time consuming calibration session. This can be achieved by co-adaptation between the brain and the BCI system so as to cooperatively achieve the best performance in a task. In order to address the aim of this project, we will explore novel adaptive signal processing and machine learning algorithms coupled with human training strategies to facilitate the usage of BCI.
- A good degree (a first or upper second class UK honours degree or equivalent qualifications gained outside the UK) in engineering, mathematics, computer science or subjects where signal processing and machine learning are applied
- Residency restrictions – awards are open to UK and EU applicants only.
Contact Dr Mahnaz Arvaneh on firstname.lastname@example.org sending a CV with a cover letter detailing your interests and suitability for the role. Please mention ASTAR-Sheffield PhD in the subject of your email.
Supports Home / EU fees and a maintenance stipend at the RCUK rate.
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