The effects of electrical field spatial spread and some cognitive factors on speech-in-noise performance of individual cochlear implant users—A computer model study

Abstract

The relation of the individual speech-in-noise performance differences in cochlear implant (CI) users to underlying physiological factors is currently poorly understood. This study approached this research question by a step-wise individualization of a computer model of speech intelligibility mimicking the details of CI signal processing and some details of the physiology present in CI users. Two factors, the electrical field spatial spread and internal noise (as a coarse model of the individual cognitive performance) were incorporated. Internal representations of speech-in-noise mixtures calculated by the model were classified using an automatic speech recognizer backend employing Hidden Markov Models with a Gaussian probability distribution. One-dimensional electric field spatial spread functions were inferred from electrical field imaging data of 14 CI users. Simplified assumptions of homogenously distributed auditory nerve fibers along the cochlear array and equal distance between electrode array and nerve tissue were assumed in the model. Internal noise, whose standard deviation was adjusted based on either anamnesis data, or text-reception-threshold data, or a combination thereof, was applied to the internal representations before classification. A systematic model evaluation showed that predicted speech-reception-thresholds (SRTs) in stationary noise improved (decreased) with decreasing internal noise standard deviation and with narrower electric field spatial spreads. The model version that was individualized to actual listeners using internal noise alone (containing average spatial spread) showed significant correlations to measured SRTs, reflecting the high correlation of the text-reception threshold data with SRTs. However, neither individualization to spatial spread functions alone, nor a combined individualization based on spatial spread functions and internal noise standard deviation did produce significant correlations with measured SRTs.

Assessing the relationship between neural health measures and speech performance with simultaneous electric stimulation in cochlear implant listeners

“…Results from the EFI recording were constructed by omitting the recorded potential at the stimulating electrode and fitting an exponential curve based on the data of the fifteen recording electrodes. The resulting peak of the exponential fit was used to determine the width of the curve 3 dB below this point in mm [ 39 , 40 , 42 ]. Most extreme electrodes were created by mirroring the side towards the middle of the array.…”

“…This component of the ENI is related to the tissue electrical properties [ 39 ] and it reflects the voltage spread in the cochlea. Moreover, it has been suggested that the voltage distribution may reflect the electrode position relative to the inner wall [ 40 , 41 ] and thus hypothesized that it could explain some variability in speech performance [ 42 ]. We hypothesize that CI listeners who have narrow voltage spread across the electrode array will be more resistant in terms of performance decrements in speech understanding when increased levels of channel interaction are applied by increasing number of simultaneously stimulated pairs of electrodes.…”

A phenomenological computational model of the evoked action potential fitted to human cochlear implant responses

“…Currently, there are different computational models of CIs that have been fitted using clinical data, such as transimpedances, cochlear dimensions, electrode locations, and loudness and pitch perception [ 10 , 25 , 27 , 28 ], and speech understanding [ 29 ]. Additionally, there are other models that simulate ECAPs responses [ 14 , 16 , 22 , 30 – 32 ] as the one proposed in this work.…”

Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants

“…Proposed an analytical model to predict loudness growth for different electrode configurations [ 29 , 30 ]. Developed a computational model of pitch perception and discussed the contribution of place and temporal cues to pitch perception [ 31 , 32 ]. Demonstrated how the spatial spread of the stimulation field impacts on speech recognition and predicted speech intelligibility.…”