Author(s)

W. K. Lai^1,2, N. Dillier¹, M. Killian³

¹ENT Clinic, University Hospital, Zürich, Switzerland.
²Sydney Cochlear Implant Centre, RIDBC, Sydney, Australia.
³Cochlear Technology Centre, Mechelen, Belgium.

A novel cochlear implant coding strategy based on the neural excitability has been developed and implemented using Matlab/Simulink. Unlike present day coding strategies, the Excitability Controlled Coding (ECC) strategy uses a model of the excitability state of the target neural population to determine its stimulus selection, with the aim of more efficient stimulation as well as reduced channel interaction. Central to the ECC algorithm is an excitability state model, which takes into account the supposed refractory behaviour of the stimulated neural populations. The excitability state, used to weight the input signal for selecting the stimuli, is estimated and updated after the presentation of each stimulus, and used iteratively in selecting the next stimulus. Additionally, ECC regulates the frequency of stimulation on a given channel as a function of the corresponding input stimulus intensity. Details of the model, implementation and results of benchtop plus subjective tests are presented and discussed. Compared to the Advanced Combination Encoder (ACE) strategy, ECC produces a better spectral representation of an input signal, and can potentially reduce channel interactions. Pilot test results from 4 CI recipients suggest that ECC may have some advantage over ACE for complex situations such as speech in noise, possibly due to ECC’s ability to present more of the input spectral contents compared to ACE, which is restricted to a fixed number of maxima. The ECC strategy represents a neuro-physiological approach that could potentially improve the perception of more complex sound patterns with cochlear implants.

Cochlear Implants, Speech Coding, Auditory, Neural Excitability, Channel Interaction

Lai, W. , Dillier, N. and Killian, M. (2018) A Neural Excitability Based Coding Strategy for Cochlear Implants. Journal of Biomedical Science and Engineering, 11, 159-181. doi: 10.4236/jbise.2018.117014.