Automatic calibration of electrooculography data for accurate quantification of eye-movements (NEWMANJ_U26CMP)
About the Project
Dizziness affects up to 20% of the population, often the elderly, placing time and cost pressures on health services worldwide. Vertigo, a type of dizziness, results in an unusual eye-movement which can assist diagnosis, but is often not present by the time patients visit their clinician. Diagnosis is challenging, as there are no objective tests and self-reporting is subjective. With previous funding from the UKRI, we have developed the CAVA device to provide continuous, month-long recordings of eye-movements from patients [1]. Using electrode pads adhered to the face, it captures the electrical signals produced by the eyes, using a technology called Electrooculography. Computer algorithms then detect abnormal eye movements, enabling clinicians to provide a retrospective diagnosis.
The signals recorded by CAVA vary according to several factors such as individual physiology, ambient light, and positioning of the electrode pads [2]. A calibration step is required to account for these. A significant challenge is posed by calibrating in non-clinical settings, where patients are not assisted by a professional and the environment may change frequently throughout the day. Uncalibrated data has previously been sufficient to make observations about the presence or absence of dizziness, but the problem of calibration must now be tackled to allow more meaningful and useful measurements to be made. In this project, you will develop neural networks (AI) capable of learning the vast and complex features of eye-movement, for automatic and continuous calibration, in the home.
The School of Computing Sciences (https://www.uea.ac.uk/about/school-of-computing-sciences) provides a vibrant research environment for conducting Computing and allied research and training. We collaborate with multi-national companies such as Apple, BT, the National Trust and Aviva, research institutes in the Norwich Research Park (https://www.norwichresearchpark.com), as well as other universities and industries in the UK and overseas. We are also members of the Turing University Network, a group of 65 UK universities working together to advance world-class research and build skills for the future.
The successful candidate will also be expected to contribute to Tutor activities for laboratory support on our BSc and MSc Courses in Artificial Intelligence, Data Science, Computing Sciences and Cyber Security commensurate with their core expertise, within the working hours permitted for full-time Postgraduate Researchers.
[1] Phillips, J. S., Newman, J. L., & Cox, S. J. (2019). An investigation into the diagnostic accuracy, reliability, acceptability and safety of a novel device for Continuous Ambulatory Vestibular Assessment (CAVA). *Scientific reports*, *9*(1), 10452.
[2] Nezvadovitz, J. R., & Rao, H. M. (2022). Using natural head movements to continually calibrate eog signals. *Journal of Eye Movement Research*, *15*(5), 35.
Funding Notes
This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees, an annual stipend for 3 years and a research training support budget.
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