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Enhancing cognitive fitness and attention for active mathematical learning through neural engineering


About This PhD Project

Project Description

- Basic Funding details: Full time Home/EU fees and a stipend of £15,009 p.a.
- Application deadline: Tuesday 7th May 2019
- Start date: October 2019
- Duration: 3 years full-time
- Location: Colchester campus
- The lead department and any connected depts.: Computer Science and Electronic Engineering (lead), Psychology, Mathematical Sciences

A brief overview of the studentship:


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.

Detailed funding information
The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (see https://www1.essex.ac.uk/fees-and-funding/research/default.aspx for 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.

Supervisors


Lead Supervisor:
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. Please see http://scholar.google.at/citations?user=E_wVwfUAAAAJ&hl and http://orcid.org/0000-0003-3407-9709 for information about his research output.

Co-Supervisors:
• 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”.
• Dr Ian Daly is a lecturer in Brain-Computer Interfaces in the department of Computer Science and Electronic Engineering in the University of Essex 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.
• 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. body dissatisfaction or maths anxiety) and brain activity in response to relevant triggers (e.g. picture of bodies or maths puzzles).

Criteria:


Essential skills of the successful candidate
Computer programming. This may include the following skills (or equivalent to them):
• Unity
• Python
• Web development
• Matlab for data processing

Desirable skills
• 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

In addition to the usual documents in the application, we would like the applicant to provide:
• Examples of their recent code development, for example, screenshots or links to GitHub.
• A short description of how they would test if a game like Lumosity is benefiting the player’s cognitive fitness.

For more information and details on how to apply please follow this link https://www.essex.ac.uk/postgraduate-research-degrees/opportunities/Enhancing-cognitive-fitness

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