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

Spatially precise activation of the mouse cochlea with a multi-channel hybrid cochlear implant

Objective.Cochlear implants are among the few clinical interventions for people with severe or profound hearing loss. However, current spread during monopolar electrical stimulation results in poor spectral resolution, prompting the exploration of optical stimulation as an alternative approach. Enabled by introducing light-sensitive ion channels into auditory neurons (optogenetics), optical stimulation has been shown to activate a more discrete neural area with minimal overlap between each frequency channel during simultaneous stimulation. However, the utility of optogenetic approaches is uncertain due to the low fidelity of responses to light and high-power requirements compared to electrical stimulation.Approach.Hybrid stimulation, combining sub-threshold electrical and optical pulses, has been shown to improve fidelity and use less light, but the impact on spread of activation and channel summation using a translatable, multi-channel hybrid implant is unknown. This study examined these factors during single channel and simultaneous multi-channel hybrid stimulation in transgenic mice expressing the ChR2/H134R opsin. Acutely deafened mice were implanted with a hybrid cochlear array containing alternating light emitting diodes and platinum electrode rings. Spiking activity in the inferior colliculus was recorded during electrical-only or hybrid stimulation in which optical and electrical stimuli were both at sub-threshold intensities. Thresholds, spread of activation, and threshold shifts during simultaneous hybrid stimulation were compared to electrical-only stimulation.Main results.The electrical current required to reach activation threshold during hybrid stimulation was reduced by 7.3 dB compared to electrical-only stimulation (p< 0.001). The activation width measured at two levels of discrimination above threshold and channel summation during simultaneous hybrid stimulation were significantly lower compared to electrical-only stimulation (p< 0.05), but there was no spatial advantage of hybrid stimulation at higher electrical stimulation levels.Significance.Reduced channel interaction would facilitate multi-channel simultaneous stimulation, thereby enhancing the perception of temporal fine structure which is crucial for music and speech in noise.

A full-head model to investigate intra and extracochlear electric fields in cochlear implant stimulation

Objective.Despite the widespread use and technical improvement of cochlear implant (CI) devices over past decades, further research into the bioelectric bases of CI stimulation is still needed. Various stimulation modes implemented by different CI manufacturers coexist, but their true clinical benefit remains unclear, probably due to the high inter-subject variability reported, which makes the prediction of CI outcomes and the optimal fitting of stimulation parameters challenging. A highly detailed full-head model that includes a cochlea and an electrode array is developed in this study to emulate intracochlear voltages and extracochlear current pathways through the head in CI stimulation.Approach.Simulations based on the finite element method were conducted under monopolar, bipolar, tripolar (TP), and partial TP modes, as well as for apical, medial, and basal electrodes. Variables simulated included: intracochlear voltages, electric field (EF) decay, electric potentials at the scalp and extracochlear currents through the head. To better understand CI side effects such as facial nerve stimulation, caused by spurious current leakage out from the cochlea, special emphasis is given to the analysis of the EF over the facial nerve.Main results.The model reasonably predicts EF magnitudes and trends previously reported in CI users. New relevant extracochlear current pathways through the head and brain tissues have been identified. Simulated results also show differences in the magnitude and distribution of the EF through different segments of the facial nerve upon different stimulation modes and electrodes, dependent on nerve and bone tissue conductivities.Significance.Full-head models prove useful tools to model intra and extracochlear EFs in CI stimulation. Our findings could prove useful in the design of future experimental studies to contrast FNS mechanisms upon stimulation of different electrodes and CI modes. The full-head model developed is freely available for the CI community for further research and use.

Fraxicon for Optical Applications with Aperture ∼1 mm: Characterisation Study

Emerging applications of optical technologies are driving the development of miniaturised light sources, which in turn require the fabrication of matching micro-optical elements with sub-1 mm cross-sections and high optical quality. This is particularly challenging for spatially constrained biomedical applications where reduced dimensionality is required, such as endoscopy, optogenetics, or optical implants. Planarisation of a lens by the Fresnel lens approach was adapted for a conical lens (axicon) and was made by direct femtosecond 780 nm/100 fs laser writing in the SZ2080™ polymer with a photo-initiator. Optical characterisation of the positive and negative fraxicons is presented. Numerical modelling of fraxicon optical performance under illumination by incoherent and spatially extended light sources is compared with the ideal case of plane-wave illumination. Considering the potential for rapid replication in soft polymers and resists, this approach holds great promise for the most demanding technological applications.

Spread of activation and interaction between channels with multi-channel optogenetic stimulation in the mouse cochlea

For individuals with severe to profound hearing loss resulting from irreversibly damaged hair cells, cochlear implants can be used to restore hearing by delivering electrical stimulation directly to the spiral ganglion neurons. However, current spread lowers the spatial resolution of neural activation. Since light can be easily confined, optogenetics is a technique that has the potential to improve the precision of neural activation, whereby visible light is used to stimulate neurons that are modified with light-sensitive opsins. This study compares the spread of neural activity across the inferior colliculus of the auditory midbrain during electrical and optical stimulation in the cochlea of acutely deafened mice with opsin-modified spiral ganglion neurons (H134R variant of the channelrhodopsin-2). Monopolar electrical stimulation was delivered via each of four 0.2 mm wide platinum electrode rings at 0.6 mm centre-to-centre spacing, whereas 453 nm wavelength light was delivered via each of five 0.22 × 0.27 mm micro-light emitting diodes (LEDs) at 0.52 mm centre-to-centre spacing. Channel interactions were also quantified by threshold changes during simultaneous stimulation by pairs of electrodes or micro-LEDs at different distances between the electrodes (0.6, 1.2 and 1.8 mm) or micro-LEDs (0.52, 1.04, 1.56 and 2.08 mm). The spread of activation resulting from single channel optical stimulation was approximately half that of monopolar electrical stimulation as measured at two levels of discrimination above threshold (p<0.001), whereas there was no significant difference between optical stimulation in opsin-modified deafened mice and pure tone acoustic stimulation in normal-hearing mice. During simultaneous micro-LED stimulation, there were minimal channel interactions for all micro-LED spacings tested. For neighbouring micro-LEDs/electrodes, the relative influence on threshold was 13-fold less for optical stimulation compared electrical stimulation (p<0.05). The outcomes of this study show that the higher spatial precision of optogenetic stimulation results in reduced channel interaction compared to electrical stimulation, which could increase the number of independent channels in a cochlear implant. Increased spatial resolution and the ability to activate more than one channel simultaneously could lead to better speech perception in cochlear implant recipients.

En route to sound coding strategies for optical cochlear implants

Hearing loss is the most common human sensory deficit. Severe-to-complete sensorineural hearing loss is often treated by electrical cochlear implants (eCIs) bypassing dysfunctional or lost hair cells by direct stimulation of the auditory nerve. The wide current spread from each intracochlear electrode array contact activates large sets of tonotopically organized neurons limiting spectral selectivity of sound coding. Despite many efforts, an increase in the number of independent eCI stimulation channels seems impossible to achieve. Light, which can be better confined in space than electric current may help optical cochlear implants (oCIs) to overcome eCI shortcomings. In this review, we present the current state of the optogenetic sound encoding. We highlight optical sound coding strategy development capitalizing on the optical stimulation that requires fine-grained, fast, and power-efficient real-time sound processing controlling dozens of microscale optical emitters as an emerging research area.

A computational model to simulate spectral modulation and speech perception experiments of cochlear implant users

Speech understanding in cochlear implant (CI) users presents large intersubject variability that may be related to different aspects of 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 the speech performance of CI users in an environment where all these physiological aspects can be controlled. In this study, 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 threshold (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.

Model-based prediction of optogenetic sound encoding in the human cochlea by future optical cochlear implants

When hearing fails, electrical cochlear implants (eCIs) partially restore hearing by direct stimulation of spiral ganglion neurons (SGNs). As light can be better confined in space than electrical current, optical CIs (oCIs) provide more spectral information promising a fundamental improvement of hearing restoration by cochlear implants. Here, we turned to computer modelling for predicting the outcome of optogenetic hearing restoration by future oCIs in humans. We combined three-dimensional reconstruction of the human cochlea with ray-tracing simulation of emission from LED or laser-coupled waveguide emitters of the oCI. Irradiance was read out at the somata of SGNs. The irradiance values reached with waveguides were about 14 times higher than with LEDs, at the same radiant flux of the emitter. Moreover, waveguides outperformed LEDs regarding spectral selectivity. oCIs with either emitter type showed greater spectral selectivity when compared to eCI. In addition, modeling the effects of the source-to-SGN distance, orientation of the sources and impact of scar tissue further informs the development of optogenetic hearing restoration.

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

There is a growing interest in biomedical engineering in developing procedures that provide accurate simulations of the neural response to electrical stimulus produced by implants. Moreover, recent research focuses on models that take into account individual patient characteristics.

We present a phenomenological computational model that is customized with the patient’s data provided by the electrically evoked compound action potential (ECAP) for simulating the neural response to electrical stimulus produced by the electrodes of cochlear implants (CIs). The model links the input currents of the electrodes to the simulated ECAP.

Potentials and currents are calculated by solving the quasi-static approximation of the Maxwell equations with the finite element method (FEM). In ECAPs recording, an active electrode generates a current that elicits action potentials in the surrounding auditory nerve fibers (ANFs). The sum of these action potentials is registered by other nearby electrode. Our computational model emulates this phenomenon introducing a set of line current sources replacing the ANFs by a set of virtual neurons (VNs). To fit the ECAP amplitudes we assign a suitable weight to each VN related with the probability of an ANF to be excited. This probability is expressed by a cumulative beta distribution parameterized by two shape parameters that are calculated by means of a differential evolution algorithm (DE). Being the weights function of the current density, any change in the design of the CI affecting the current density produces changes in the weights and, therefore, in the simulated ECAP, which confers to our model a predictive capacity.

The results of the validation with ECAP data from two patients are presented, achieving a satisfactory fit of the experimental data with those provided by the proposed computational model.

The cochlea, found in the inner ear, is the organ where the sound is transformed into an electrical pulse to be transmitted by the neurons to the auditory cortex. Hearing loss can be caused by damage to the hair cells, in which case neuronal excitation is impaired. CIs are devices that replace the normal function of the impaired/damaged Organ of Corti. Computational models allow a better understanding of the mechanisms involved in the electrical stimulation of the auditory nerve. These models can help biomedical engineers to develop new CIs with improved auditory performance. One important aspect of our model is its customization with the patient’s data provided by the recording of the evoked compound action potential (the synchronous firing of a population of electrically stimulated auditory nerve fibers). This phenomenological model allows us to predict the registers of neural stimulation produced when the auditory nerve is stimulated with the CIs. We have validated the proposed model with real data obtained from two patients with CIs.

Modelling speech reception thresholds and their improvements due to spatial noise reduction algorithms in bimodal cochlear implant users

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This paper compares two modelling approaches to predict the speech recognition ability of bimodal CI users and the benefit of using beamformers.

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The modelling approaches vary in computational complexity and fitting requirements.

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A complex cafeteria spatial scenario with three localized single noise source scenario and a diffuse multi-talker babble noise is used.

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The automatic speech recognizer is more accurate across the different spatial scenarios and noise types and requires less fitting compared to the statistical modelling approach.

Spatial noise reduction algorithms (“beamformers”) can considerably improve speech reception thresholds (SRTs) for bimodal cochlear implant (CI) users. The goal of this study was to model SRTs and SRT-benefit due to beamformers for bimodal CI users. Two existing model approaches varying in computational complexity and binaural processing assumption were compared: (i) the framework of auditory discrimination experiments (FADE) and (ii) the binaural speech intelligibility model (BSIM), both with CI and aided hearing-impaired front-ends. The exact same acoustic scenarios, and open-access beamformers as in the comparison clinical study Zedan et al. (2021) were used to quantify goodness of prediction. FADE was capable of modeling SRTs ab-initio, i.e., no calibration of the model was necessary to achieve high correlations and low root-mean square errors (RMSE) to both, measured SRTs (r = 0.85, RMSE = 2.8 dB) and to measured SRT-benefits (r = 0.96). BSIM achieved somewhat poorer predictions to both, measured SRTs (r = 0.78, RMSE = 6.7 dB) and to measured SRT-benefits (r = 0.91) and needs to be calibrated for matching average SRTs in one condition. Greatest deviations in predictions of BSIM were observed in diffuse multi-talker babble noise, which were not found with FADE. SRT-benefit predictions of both models were similar to instrumental signal-to-noise ratio (iSNR) improvements due to the beamformers. This indicates that FADE is preferrable for modeling absolute SRTs. However, for prediction of SRT-benefit due to spatial noise reduction algorithms in bimodal CI users, the average iSNR is a much simpler approach with similar performance.

Intra-Cochlear Current Spread Correlates with Speech Perception in Experienced Adult Cochlear Implant Users

Broader intra-cochlear current spread (ICCS) implies higher cochlear implant (CI) channel interactions. This study aimed to investigate the relationship between ICCS and speech intelligibility in experienced CI users. Using voltage matrices collected for impedance measurements, an individual exponential spread coefficient (ESC) was computed. Speech audiometry was performed to determine the intelligibility at 40 dB Sound Pressure Level (SPL) and the 50% speech reception threshold: I40 and SRT50 respectively. Correlations between ESC and either I40 or SRT50 were assessed. A total of 36 adults (mean age: 50 years) with more than 11 months (mean: 34 months) of CI experience were included. In the 21 subjects for whom all electrodes were active, ESC was moderately correlated with both I40 (r = −0.557, p = 0.009) and SRT50 (r = 0.569, p = 0.007). The results indicate that speech perception performance is negatively affected by the ICCS. Estimates of current spread at the closest vicinity of CI electrodes and prior to any activation of auditory neurons are indispensable to better characterize the relationship between CI stimulation and auditory perception in cochlear implantees.

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

OBJECTIVES

The relationship between electrode-nerve interface (ENI) estimates and inter-subject differences in speech performance with sequential and simultaneous channel stimulation in adult cochlear implant listeners were explored. We investigated the hypothesis that individuals with good ENIs would perform better with simultaneous compared to sequential channel stimulation speech processing strategies than those estimated to have poor ENIs.

METHODS

Fourteen postlingually deaf implanted cochlear implant users participated in the study. Speech understanding was assessed with a sentence test at signal-to-noise ratios that resulted in 50% performance for each user with the baseline strategy F120 Sequential. Two simultaneous stimulation strategies with either two (Paired) or three sets of virtual channels (Triplet) were tested at the same signal-to-noise ratio. ENI measures were estimated through: (I) voltage spread with electrical field imaging, (II) behavioral detection thresholds with focused stimulation, and (III) slope (IPG slope effect) and 50%-point differences (dB offset effect) of amplitude growth functions from electrically evoked compound action potentials with two interphase gaps.

RESULTS

A significant effect of strategy on speech understanding performance was found, with Triplets showing a trend towards worse speech understanding performance than sequential stimulation. Focused thresholds correlated positively with the difference required to reach most comfortable level (MCL) between Sequential and Triplet strategies, an indirect measure of channel interaction. A significant offset effect (difference in dB between 50%-point for higher eCAP growth function slopes with two IPGs) was observed. No significant correlation was observed between the slopes for the two IPGs tested. None of the measures used in this study correlated with the differences in speech understanding scores between strategies.

CONCLUSIONS

The ENI measure based on behavioral focused thresholds could explain some of the difference in MCLs, but none of the ENI measures could explain the decrease in speech understanding with increasing pairs of simultaneously stimulated electrodes in processing strategies.

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

Despite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that cochlear implant user-dependent factors limit speech perception at the individual level. Clinical studies have demonstrated the importance of the number, placement, and insertion depths of electrodes on speech recognition abilities. However, these do not account for all inter-subject variability and to what extent these factors affect speech recognition abilities has not been studied. In this paper, an information theoretic method and machine learning technique are unified in a model to investigate the extent to which key factors limit cochlear implant electrode discrimination. The framework uses a neural network classifier to predict which electrode is stimulated for a given simulated activation pattern of the auditory nerve, and mutual information is then estimated between the actual stimulated electrode and predicted ones. We also investigate how and to what extent the choices of parameters affect the performance of the model. The advantages of this framework include i) electrode discrimination ability is quantified using information theory, ii) it provides a flexible framework that may be used to investigate the key factors that limit the performance of cochlear implant users, and iii) it provides insights for future modeling studies of other types of neural prostheses.

Prediction of Individual Cochlear Implant Recipient Speech Perception With the Output Signal to Noise Ratio Metric

OBJECTIVES

A cochlear implant (CI) implements a variety of sound processing algorithms that seek to improve speech intelligibility. Typically, only a small number of parameter combinations are evaluated with recipients but the optimal configuration may differ for individuals. The present study evaluates a novel methodology which uses the output signal to noise ratio (OSNR) to predict complete psychometric functions that relate speech recognition to signal to noise ratio for individual CI recipients.

DESIGN

Speech scores from sentence-in-noise tests in a "reference" condition were mapped to OSNR and a psychometric function was fitted. The reference variability was defined as the root mean square error between the reference scores and the fitted curve. To predict individual scores in a different condition, OSNRs in that condition were calculated and the corresponding scores were read from the reference psychometric function. In a retrospective experiment, scores were predicted for each condition and subject in three existing data sets of sentence scores. The prediction error was defined as the root mean square error between observed and predicted scores. In data set 1, sentences were mixed with 20 talker babble or speech weighted noise and presented at 65 dB sound pressure level (SPL). An adaptive test procedure was used. Sound processing was advanced combinatorial encoding (ACE, Cochlear Limited) and ACE with ideal binary mask processing, with five different threshold settings. In data set 2, sentences were mixed with speech weighted noise, street-side city noise or cocktail party noise and presented at 65 dB SPL. An adaptive test procedure was used. Sound processing was ACE and ACE with two different noise reduction schemes. In data set 3, sentences were mixed with four-talker babble at two input SNRs and presented at levels of 55-89 dB SPL. Sound processing utilised three different automatic gain control configurations.

RESULTS

For data set 1, the median of individual prediction errors across all subjects, noise types and conditions, was 12% points, slightly better than the reference variability. The OSNR prediction method was inaccurate for the specific condition with a gain threshold of +10 dB. For data set 2, the median of individual prediction errors was 17% points and the reference variability was 11% points. For data set 3, the median prediction error was 9% points and the reference variability was 7% points. A Monte Carlo simulation found that the OSNR prediction method, which used reference scores and OSNR to predict individual scores in other conditions, was significantly more accurate (p < 0.01) than simply using reference scores as predictors.

CONCLUSIONS

The results supported the hypothesis that the OSNR prediction method could accurately predict individual recipient scores for a range of algorithms and noise types, for all but one condition. The medians of the individual prediction errors for each data set were accurate within 6% points of the reference variability and compared favourably with prediction methodologies in other recent studies. Overall, the novel OSNR-based prediction method shows promise as a tool to assist researchers and clinicians in the development or fitting of CI sound processors.

Modeling Pitch Perception With an Active Auditory Model Extended by Octopus Cells

Pitch is an essential category for musical sensations. Models of pitch perception are vividly discussed up to date. Most of them rely on definitions of mathematical methods in the spectral or temporal domain. Our proposed pitch perception model is composed of an active auditory model extended by octopus cells. The active auditory model is the same as used in the Stimulation based on Auditory Modeling (SAM), a successful cochlear implant sound processing strategy extended here by modeling the functional behavior of the octopus cells in the ventral cochlear nucleus and by modeling their connections to the auditory nerve fibers (ANFs). The neurophysiological parameterization of the extended model is fully described in the time domain. The model is based on latency-phase en- and decoding as octopus cells are latency-phase rectifiers in their local receptive fields. Pitch is ubiquitously represented by cascaded firing sweeps of octopus cells. Based on the firing patterns of octopus cells, inter-spike interval histograms can be aggregated, in which the place of the global maximum is assumed to encode the pitch.