Cochlear
implants (CIs) are successful auditory prostheses that enable people
with deafness to hear
through electrical stimulation of the auditory nerve. In a CI sound processor, a sound signal is converted into a sequence of electrical pulses. This conversion entails many parameters that should ideally be fine-tuned (fitted) for every individual patient, to account for various anatomical and physiological differences. In current clinical practice, devices are fitted during the initial rehabilitation and yearly thereafter. As fitting is very time consuming, only the bare minimum number of parameters is fitted individually. However, for many other parameters, for which currently the same default values are used for all patients, better speech understanding can be achieved with individual fitting. Apart from the fitting, CIs do not take into account the neural or perceptual effects of stimulation.
through electrical stimulation of the auditory nerve. In a CI sound processor, a sound signal is converted into a sequence of electrical pulses. This conversion entails many parameters that should ideally be fine-tuned (fitted) for every individual patient, to account for various anatomical and physiological differences. In current clinical practice, devices are fitted during the initial rehabilitation and yearly thereafter. As fitting is very time consuming, only the bare minimum number of parameters is fitted individually. However, for many other parameters, for which currently the same default values are used for all patients, better speech understanding can be achieved with individual fitting. Apart from the fitting, CIs do not take into account the neural or perceptual effects of stimulation.
Recently
prof.
Tom Francart obtained
a grant of 1.7M EUR from the European Commission (ERC starting grant
ISIFit), which gave us the unique opportunity to setup a research
project on individualized objective fitting of cochlear implants.
Five researchers will work on his project, which started in November
2015 and will run for five years.
The
objective of this project is to provide better fitting to
individual patients
by developing a closed-loop CI that automatically adjusts its fitting
and sound processing based on the neural response to speech. To
achieve this, we will (1) objectively measure speech understanding,
by recording
the electroencephalogram (EEG) in response to ecological speech
signals, (2) automatically fit a wide
array of sound processing parameters accordingly using a genetic
algorithm, and (3) develop a wearable
closed-loop CI that continuously records the EEG and adjusts the
fitting in real-life situations.
This will lead to a better understanding of speech perception of
people with
a hearing impairment, an objective measure of speech intelligibility
with many applications in diagnostics,
a method to automatically fit CIs, and a novel closed-loop CI. For
the patient this means
improved speech intelligibility in noise and therefore better
communication and quality of life. For the clinic
this means improved efficiency and the ability to better fit devices.
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