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.

This study investigates the effect of the Nucleus CI24RE implant's neural response telemetry (NRT) system, which has less internal noise compared to its predecessor, the CI24M/R implant, on the NRT threshold (TNRT) profile across the array. CI24M/R measurements were simulated by ignoring CI24RE measurements with response amplitudes below 50 uV. Comparisons of the estimated TNRTs from the CI24RE measurements and the CI24M/R simulations suggest that, apart from a constant level difference, the TNRT profiles from the newer implant generally would not have differed very much from those of its predecessor. This view was also reflected by principal component analysis (PCA) results which revealed a 'shift' component similar to that reported by Smoorenburg et al (2002). On the whole, there is no indication that current practices of using the TNRT profiles for assisting with speech processor programming need to be revised for the CI24RE implant.

Bei Kochleaimplantaten lassen sich die elektrisch evozierten Summenaktionspotenziale (TECAP) des Hörnervs messen. TECAP-Schwellen werden zur Schätzung von Schwellen bei der Sprachprozessorprogrammierung verwendet. Das Refraktärverhalten des Hörnervs kann diese beeinflussen.

Introduction. Electrically evoked compound action potentials (TECAP) of the auditory nerve can be recorded in cochlear implants. TECAP thresholds are used to predict threshold levels for speech processor maps. The auditory nerve's refractory properties can influence these levels.Methods. In the award winning study [12] recovery functions were investigated at 84 stimulation sites in 14 patients who had Nucleus CI24 implants; neural response telemetry (NRT) and a modified forward-masking technique were used for these investigations, introducing the reference masker-probe interval (MPI).Results and conclusion. An interval between 300 and 375 mu s was found to be suitable as the reference MPI in our study. The median of the absolute refractory period was determined as 390 s and the median time constant of the recovery function, at 425 s. In practice, a reference MPI of 300 s is suggested for measurement of recovery and amplitude growth functions. As up to now the amplitude growth function has been measured at 500 s and thus mostly in a relatively refractory condition, the refractory behaviour should influence the TNRT. In addition, it was possible to explain the shape of standard forward-masking recovery functions with reference to the latency shift of the neural response.

The Neural Response Telemetry (NRT) system(R) provides a simplified tool for Electrically Evoked Compound Action Potential (ECAP) recordings. This system was successfully validated in humans and permits in-situ measurement of auditory neural activity within the cochlea in response to electrical stimulation and allows many clinical applications. One of the main difficulties in using this system is the time required by the clinicians for both, measure and data analysis. Data analysis requires the selection of valid neural traces. This selection cannot be done only based on the amplitude information because trace morphology cues add to the decision. In this study, we propose two different recognition methods to automatically recognize neural responses traces recorded with NRT system. The first method uses artificial neural network technology (ANN) while the second uses a cross correlation method (CC). Both systems were fed with 120 neural responses divided in five different categories associated to the main response morphologies observed with NRT recordings. For both methods, ROC curves method was used with a set of one thousand NRT traces to compare and evaluate performances. NRT traces were classified by two human experts who worked together to reduce the expert subjectivity. NRT traces were classified in three different categories depending on the clinical application expected with each trace. Results show that CC method led to a higher degree of precision than the ANN method. Particularly a score of 82% was obtained for the correct positives and correct negatives identification with a false positive rate fixed at only 2%. The results presented in this study suggest that CC method is accurate enough to be used for clinical applications.