FEIT Research Project Database

Personalised optimisation of cochlear implants

Project Leader: Demi Gao
Staff: David Grayden
Collaborators: Richard Dowell(Audiology & Speech Science), Mark McDonnell(University of South Australia) Joseph Lizier (University of Sydney)
Primary Contact: David Grayden (grayden@unimelb.edu.au)
Keywords: auditory processing; cochlear implant; machine learning; neural engineering; neuroengineering
Disciplines: Biomedical Engineering

Despite the success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that CI user-dependent factors limit speech perception at the individual level. Clinical studies have demonstrated the importance of the number and placement of electrodes and of insertion depths on speech recognition abilities, but these do not account for all inter-subject variability. Therefore, personalised configuration of CIs for individuals still largely relies on the experience of an audiologist and has not benefited from objective psychophysical measurements (e.g., pitch perception, speech recognition).

The aim of this project is to develop a system for personalised configuration of cochlear implant devices that will benefit potentially millions of cochlear implant users.  Specifically, this project aims to i) create a framework to model and study psychophysical hearing measurements and to predict the optimal device configuration for individuals, and ii) develop a mobile application for personalised device configuration to be tested by cochlear implant users.

To qualify as a PhD candidate for this research, you need to have a strong background in Biomedical Engineering, Electrical/Electronic Engineering or Computer Science and have good programming skills. You will need to have a passion to learn about hearing with cochlear implants and have a willingness to work with cochlear implant users. You will have the opportunity to work with a world-class group of engineers, biologists and physicists and have access to world-class computational infrastructure. You will be trained in literature searching, scientific writing and academic presentation skills and will have opportunities to attend relevant conferences.