Cochlear III - implants - Signal processing and automatic fitting for next generation cochlear implants
From 01-07-2008 to 30-09-2011
Description
A Cochlear Implant (CI) is a device that enables profoundly deaf people to perceive sounds byelectrically stimulating the auditory nerves in the cochlea using an implanted electrode array. During
the last years, the performance of these devices has considerably improved such that most recipients
are able to understand speech, at least in quiet environments.
However, in noisy environments (e.g. cocktail party situation, traffic) and in difficult listening
situations (e.g. interfering talkers, reverberation), CI-users still experience large problems to
understand speech. This is to some degree caused by the fact that current CI sound processing
schemes limit the perception of pitch and deteriorate the capability to localize sound sources,
compared to normal hearing persons. This is partly caused by technical limitations in the current
system and partly by the electrode-neural interface bottleneck. Hence, speech understanding in noise
can be improved by improving the spatial hearing of cochlear implantees and/or by increasing the
signal-to-noise ratio (SNR) of the incoming sound signal using sound source localisation and noise
reduction techniques that exploit the full potential of bilaterally implanted systems. For fully
implantable systems, a new noise source will be present, namely the body noise picked up by an
implanted microphone due to bone conduction. Bilaterally implanted systems and fully implantable
systems are considered to be the next generation cochlear implant systems.
The innovation goal is to develop new concepts to tackle all this, and implement these in algorithms
and software programs that can be introduced into the cochlear implant system on a newly developed
real-time research platform, so that a significant improvement in hearing performance can be made
available to new and existing CI recipients. Related to this, objective methods that obtain the best
clinical parameters in a fast and easy way for each CI recipient individually become more and more
important. Such objective methods can be used to do automatic fitting.
Team
- Marc Moonen, Co-promoter
Financing
Funding: IWT - Agentschap voor Innovatie door Wetenschap en Technologie
Program/Grant Type: IWT O&O - IWT Industrial R&D projects
Events
2/09/2024:
PhD defense - Martijn Oldenhof
Machine Learning for Advanced Chemical Analysis and Structure Recognition in Drug Discovery
3/09/2024:
Meet the Jury Igor Tetko on Advanced Machine Learning in Drug Discovery
12/09/2024:
Multimodal analysis of cell-free DNA for sensitive cancer detection in low-coverage and low-sample settings
Seminar by Antoine Passemiers
News
STADIUS Alumni Herman Verrelst – new CEO of Biocartis
08 June 2017
Herman Verrelst, the founder of KU Leuven spin-off Cartagenia, who has been working in Silicon Valley, US for the last few years will be returning to Belgium to follow the steps of Rudi Pauwels as CEO of the Belgian diagnostic company, Biocartis.
Supporting healthcare policymaking via machine learning – batteries included!
29 May 2017
STADIUS takes the lead in the data analytics efforts in an ambitious European Project MIDAS.
Marc Claesen gives an interview about his PhD for the magazine of the Faculty of Engineering Sciences "Geniaal"
10 February 2017
Did you know that in Belgium approximately one third of type 2 diabetes patients are unaware of their condition?
Joos Vandewalle is nieuwe voorzitter KVAB
09 October 2016
Op 5 oktober 2016 heeft de Algemene Vergadering van de Academie KVAB Joos Vandewalle verkozen tot voorzitter van de KVAB.