Modeling electrode place discrimination in cochlear implants: Analysis of the influence of electrode array insertion depth

Cochlear implants provide functional hearing to people who are profoundly deaf or hearing impaired by replacing the function of missing inner hair cells with an array of stimulating electrodes. Previous studies developed a modeling framework for predicting the optimal number of electrodes, as well as the optimal locations and usage probabilities of electrodes, from an information theoretic perspective. However, the information theoretic method does not quantify the performance of electrode place discrimination. In this paper, we apply a so-called `extreme-learning machine' to the cochlear implant model to calculate the electrode classification error rates. We also investigate the locations along the electrode array where errors are most likely to occur. We conclude based on our model that i) the classification error rate increases with increasing number of electrodes and the classification errors occur predominantly between adjacent electrodes, ii) by inserting the electrode array deeper into the cochlea, more electrode locations can be distinguished and the electrodes for which most errors occur are determined by the distance and spiral twirling angle between adjacent electrodes.