A computational model to simulate spectral modulation and speech perception experiments of cochlear implant users - Alvarez, Franklin; Kipping, Daniel; Nogueira, Waldo (Frontiers in Neuroinformatics, 2023)
Speech understanding in cochlear implant (CI) users presents large intersubject variability that may be related to different aspects in the peripheral auditory system, such as the electrode-nerve interface and neural health conditions. This variability makes it more challenging to proof differences in performance between different CI sound coding strategies in regular clinical studies, nevertheless, computational models can be helpful to assess speech performance of CI users in an environment where all these physiological aspects can be controlled. In this work, differences in performance between three variants of the HiRes Fidelity 120 (F120) sound coding strategy are studied with a computational model. The computational model consists of: i) a processing stage with the sound coding strategy; ii) a three-dimensional electrode-nerve interface that accounts for auditory nerve fiber (ANF) degeneration; iii) a population of phenomenological ANF models; and iv) a feature extractor algorithm to obtain the internal representation (IR) of the neural activity. As the back-end, the simulation framework for auditory discrimination experiments (FADE) was chosen. Two experiments relevant to speech understanding were performed. One related to spectral modulation threshold (SMT), and the other one related to speech reception theshold (SRT). These experiments included three different neural health conditions (healthy ANFs, and moderate and severe ANF degeneration). The F120 was configured to use sequential stimulation (F120-S), and simultaneous stimulation with two (F120-P) and three (F120-T) simultaneously active channels. Simultaneous stimulation causes electric interaction that smears the spectrotemporal information transmitted to the ANFs and it has been hypothesized to lead to even worse information transmission in poor neural health conditions. In general, worse neural health conditions led to worse predicted performance, nevertheless, the detriment was small compared to clinical data. Results in SRT experiments indicated that performance with simultaneous stimulation, especially F120-T, were more affected by neural degeneration than with sequential stimulation. Results in SMT experiments showed no significant difference in performance. Although the proposed model in its current state is able to perform SMT and SRT experiments, it is not reliable to predict real CI users' performance yet. Nevertheless, improvements related to the ANF model, feature extraction, and predictor algorithm are discussed.