Title: SCH05: Enhancing cognitive fitness and attention for active mathematical learning through neural engineering
Funding: Full time Home/EU fees and a stipend of £15,009 p.a. (terms & conditions)
Application deadline: 7 May 2019
Start date: October 2019
Duration: 3 years (full time)
Location: Colchester Campus
Based in: School of Computer Science and Electronic Engineering (in collaboration with the Department of Psychology, and the Department of Mathematical Sciences)
This studentship is now closed to applicants. View our latest opportunities.
Brain health and performance both matter. Attempts to develop effective ‘brain training’ technology has not been very successful yet due to lack of neuroscientific evidence.
This studentship will research and develop a new neuro-adaptive learning environment for mathematical education. The information technology (IT)-based learning environment will adapt the learning content based on the learner’s task performance and brain signals (electroencephalogram (EEG)).
Brain signals provide information about the learner’s mental state such as for example the level of motivation or the perceived task difficulty. Combination and processing of data from both overt and covert behaviour allows continuous adjustment of learning environment and learning material to optimize learning success (for example, increase difficulty to challenge the player).
You will conceptualize, design and implement the mathematical learning environment, adapt and develop new signal processing methods for robust estimation of motivation/task difficulty from EEG, conduct an evaluation study, and investigate what factors result in the improvement of the learner’s skill.
The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (further fee details), a doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,009 in 2019-20), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.
Lead supervisorSchool of Computer Science and Electronic Engineering, University of Essex
Reinhold Scherer is Professor in Brain-Computer Interfaces and Neural Engineering at the University of Essex, UK. Since 2001 he is working on electroencephalogram (EEG)-based brain-computer interfaces (BCI) with emphasis on online brain-machine co-adaptation to facilitate BCI use. To gain a better understanding on brain functioning and on the interpretation of EEG rhythms - essential for enhancing BCI performance - he is working on functional brain and body imaging.
Co-supervisorSchool of Computer Science and Electronic Engineering, University of Essex
Dr Ian Daly is a lecturer in Brain-Computer Interfaces and a member of the Brain-Computer Interfacing and Neural Engineering research group. His research interests include BCI, Assistive Technology, Machine learning, and Signal processing. He is also interested in semantic encoding, neurophysiological correlates of motor control, emotion, and stimuli perception and how they differ between healthy individuals and individuals with neurological and physiological impairments.
Co-supervisorDepartment of Psychology, University of Essex
Dr Helge Gillmeister is an expert in EEG methods for Cognitive Neuroscience, with a background in Psychology, Cognitive Science and Cognitive Neuroscience. She is interested in how bodily signals give rise to the sense of self, and how signal processing and machine learning techniques can be applied to map the interactions between attitudinal factors (e.g. maths anxiety) and brain activity in response to relevant triggers (e.g. maths puzzles).
Co-supervisorDepartment of Mathematical Sciences, University of Essex
Dr Alexei Vernitski conducts research in mathematical education, concentrating on how to increase learners’ motivation and reduce learners’ anxiety. His approach to teaching mathematics is based mainly on principles formulated by Jo Boaler and known as “Mathematical Mindsets”.
Essential skills of the successful candidate
Computer programming. This may include the following skills (or equivalent to them):
Experience of conducting EEG experiments and cleaning up and processing EEG data
Some knowledge of cognitive psychology
Experience with programming computer games, including computer puzzles
An interest in teaching mathematics
You can apply for this postgraduate research opportunity online.
Please upload with your application a CV, a covering letter, a personal statement and transcripts of any undergraduate or masters programmes.
In addition to these documents, we would like the applicant to provide
Instruction to applicants
When you apply online you will be prompted to fill out several boxes in the form:
If you have any informal queries about this opportunity please email the lead supervisor, Professor Reinhold Scherer (email@example.com).
You can find the terms and conditions of this studentship here (PDF).