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.
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. We developed a number of essential building blocks for a closed-loop CI:
(1) An objective measure of speech understanding, that yields an estimate of how well a person can understand speech, based on the EEG signal of the person listening to natural speech. This method was validated in normal-hearing, impaired-hearing and cochlear-implant listeners.
(2) A method to record EEG from cochlear implant electrodes, which allows chronic measurement of EEG with hardware that is already implanted
(3) A method to remove electrical CI artefacts from the EEG recording, which is crucial because the artefacts are strongly correlated with the desired signal.
When these building blocks are incorporated in a closed-loop CI, for the user this will lead to 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.
