Automatic localization of cochlear implant electrodes using cone beam computed tomography images.

BACKGROUND: Cochlear implants (CI) are implantable medical devices that enable the perception of sounds and the understanding of speech by electrically stimulating the auditory nerve in case of inner ear damage. The stimulation takes place via an array of electrodes surgically inserted in the cochlea. After CI implantation, cone beam computed tomography (CBCT) is used to evaluate the position of the electrodes. Moreover, CBCT is used in research studies to investigate the relationship between the position of the electrodes and the hearing outcome of CI user. In clinical routine, the estimation of the position of the CI electrodes is done manually, which is very time-consuming. RESULTS: The aim of this study was to optimize procedures of automatic electrode localization from CBCT data following CI implantation. For this, we analyzed the performance of automatic electrode localization for 150 CBCT data sets of 10 different types of electrode arrays. Our own implementation of the method by Noble and Dawant (Lecture notes in computer science (Including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), Springer, pp 152-159, 2015. https://doi.org/10.1007/978-3-319-24571-3_19 ) for automated electrode localization served as a benchmark for evaluation. Differences in the detection rate and the localization accuracy across types of electrode arrays were evaluated and errors were classified. Based on this analysis, we developed a strategy to optimize procedures of automatic electrode localization. It was shown that particularly distantly spaced electrodes in combination with a deep insertion can lead to apical-basal confusions in the localization procedure. This confusion prevents electrodes from being detected or assigned correctly, leading to a deterioration in localization accuracy. CONCLUSIONS: We propose an extended cost function for automatic electrode localization methods that prevents double detection of electrodes to avoid apical-basal confusions. This significantly increased the detection rate by 11.15 percent points and improved the overall localization accuracy by 0.53 mm (1.75 voxels). In comparison to other methods, our proposed cost function does not require any prior knowledge about the individual cochlea anatomy.

Phantom Stimulation for Cochlear Implant Users With Residual Low-Frequency Hearing.

OBJECTIVE: In cochlear implants (CIs), phantom stimulation can be used to extend the pitch range toward apical regions of the cochlea. Phantom stimulation consists of partial bipolar stimulation, in which current is distributed across two intracochlear electrodes and one extracochlear electrode as defined by the compensation coefficient σ. The aim of this study was, (1) to evaluate the benefit of conveying low-frequency information through phantom stimulation for cochlear implant (CI) subjects with low-frequency residual hearing using electric stimulation alone, (2) to compare the speech reception thresholds obtained from electric-acoustic stimulation (EAS) and electric stimulation in combination with phantom stimulation (EPS), and (3) to investigate the effect of spectrally overlapped bandwidth of speech conveyed via simultaneous acoustic and phantom stimulation on speech reception thresholds. DESIGN: Fourteen CI users with ipsilateral residual hearing participated in a repeated-measures design. Phantom stimulation was used to extend the frequency bandwidth of electric stimulation of EAS users towards lower frequencies without changing their accustomed electrode-frequency allocation. Three phantom stimulation configurations with different σ's were tested causing different degrees of electric field shaping towards apical regions of the cochlea that may affect the place of stimulation. A baseline configuration using a moderate value of σ () for all subjects, a configuration that was equivalent to monopolar stimulation by setting σ to 0 () and a configuration that used the largest value of σ for each individual subject (). Speech reception thresholds were measured for electric stimulation alone, EAS and EPS. Additionally, acoustic stimulation and phantom stimulation were presented simultaneously (EAS+PS) to investigate their mutual interaction. Besides the spectral overlap, the electrode insertion depth obtained from cone-beam computed-tomography scans was determined to assess the impact of spatial overlap between electric and acoustic stimulation on speech reception. RESULTS: Speech perception significantly improved by providing additional acoustic or phantom stimulation to electric stimulation. There was no significant difference between EAS and EPS. However, two of the tested subjects were able to perform the speech perception test using EAS but not using EPS. In comparison to the subject's familiar EAS listening mode, the speech perception deteriorated when acoustic stimulation and phantom stimulation conveyed spectrally overlapped information simultaneously and this deterioration increased with larger spectral overlap. CONCLUSIONS: (1) CI users with low-frequency acoustic residual hearing benefit from low-frequency information conveyed acoustically through combined EAS. (2) Improved speech reception thresholds through low-frequency information conveyed via phantom stimulation were observed for EAS subjects when acoustic stimulation was not used. (3) Speech perception was negatively affected by combining acoustic and phantom stimulation when both stimulation modalities overlapped spectrally in comparison to the familiar EAS.

Effect of Spectral Contrast Enhancement on Speech-on-Speech Intelligibility and Voice Cue Sensitivity in Cochlear Implant Users.

OBJECTIVES: Speech intelligibility in the presence of a competing talker (speech-on-speech; SoS) presents more difficulties for cochlear implant (CI) users compared with normal-hearing listeners. A recent study implied that these difficulties may be related to CI users' low sensitivity to two fundamental voice cues, namely, the fundamental frequency (F0) and the vocal tract length (VTL) of the speaker. Because of the limited spectral resolution in the implant, important spectral cues carrying F0 and VTL information are expected to be distorted. This study aims to address two questions: (1) whether spectral contrast enhancement (SCE), previously shown to enhance CI users' speech intelligibility in the presence of steady state background noise, could also improve CI users' SoS intelligibility, and (2) whether such improvements in SoS from SCE processing are due to enhancements in CI users' sensitivity to F0 and VTL differences between the competing talkers. DESIGN: The effect of SCE on SoS intelligibility and comprehension was measured in two separate tasks in a sample of 14 CI users with Cochlear devices. In the first task, the CI users were asked to repeat the sentence spoken by the target speaker in the presence of a single competing talker. The competing talker was the same target speaker whose F0 and VTL were parametrically manipulated to obtain the different experimental conditions. SoS intelligibility, in terms of the percentage of correctly repeated words from the target sentence, was assessed using the standard advanced combination encoder (ACE) strategy and SCE for each voice condition. In the second task, SoS comprehension accuracy and response times were measured using the same experimental setup as in the first task, but with a different corpus. In the final task, CI users' sensitivity to F0 and VTL differences were measured for the ACE and SCE strategies. The benefit in F0 and VTL discrimination from SCE processing was evaluated with respect to the improvement in SoS perception from SCE. RESULTS: While SCE demonstrated the potential of improving SoS intelligibility in CI users, this effect appeared to stem from SCE improving the overall signal to noise ratio in SoS rather than improving the sensitivity to the underlying F0 and VTL differences. A second key finding of this study was that, contrary to what has been observed in a previous study for childlike voice manipulations, F0 and VTL manipulations of a reference female speaker (target speaker) toward male-like voices provided a small but significant release from masking for the CI users tested. CONCLUSIONS: The present findings, together with those previously reported in the literature, indicate that SCE could serve as a possible background-noise-reduction strategy in commercial CI speech processors that could enhance speech intelligibility especially in the presence of background talkers that have longer VTLs compared with the target speaker.

A subjective and objective evaluation of a codec for the electrical stimulation patterns of cochlear implants.

Wireless transmission of audio from or to signal processors of cochlear implants (CIs) is used to improve speech understanding of CI users. This transmission requires wireless communication to exchange the necessary data. Because they are battery powered devices, energy consumption needs to be kept low in CIs, therefore making bitrate reduction of the audio signals necessary. Additionally, low latency is essential. Previously, a codec for the electrodograms of CIs, called the Electrocodec, was proposed. In this work, a subjective evaluation of the Electrocodec is presented, which investigates the impact of the codec on monaural speech performance. The Electrocodec is evaluated with respect to speech recognition and quality in ten CI users and compared to the Opus audio codec. Opus is a low latency and low bitrate audio codec that best met the CI requirements in terms of bandwidth, bitrate, and latency. Achieving equal speech recognition and quality as Opus, the Electrocodec achieves lower mean bitrates than Opus. Actual rates vary from 24.3 up to 53.5 kbit/s, depending on the codec settings. While Opus has a minimum algorithmic latency of 5 ms, the Electrocodec has an algorithmic latency of 0 ms.

Interaction Between Electric and Acoustic Stimulation Influences Speech Perception in Ipsilateral EAS Users.

OBJECTIVES: The aim of this study was to determine electric-acoustic masking in cochlear implant users with ipsilateral residual hearing and different electrode insertion depths and to investigate the influence on speech reception. The effects of different fitting strategies-meet, overlap, and a newly developed masking adjusted fitting (UNMASKfit)-on speech reception are compared. If electric-acoustic masking has a detrimental effect on speech reception, the individualized UNMASKfit map might be able to reduce masking and thereby enhance speech reception. DESIGN: Fifteen experienced MED-EL Flex electrode recipients with ipsilateral residual hearing participated in a crosssover design study using three fitting strategies for 4 weeks each. The following strategies were compared: (1) a meet fitting, dividing the frequency range between electric and acoustic stimulation, (2) an overlap fitting, delivering part of the frequency range both acoustically and electrically, and (3) the UNMASKfit, reducing the electric stimulation according to the individual electric-on-acoustic masking strength. A psychoacoustic masking procedure was used to measure the changes in acoustic thresholds due to the presence of electric maskers. Speech reception was measured in noise with the Oldenburg Matrix Sentence test. RESULTS: Behavioral thresholds of acoustic probe tones were significantly elevated in the presence of electric maskers. A maximum of masking was observed when the difference in location between the electric and acoustic stimulation was around one octave in place frequency. Speech reception scores and strength of masking showed a dependency on residual hearing, and speech reception was significantly reduced in the overlap fitting strategy. Electric- acoustic stimulation significantly improved speech reception over electric stimulation alone, with a tendency toward a larger benefit with the UNMASKfit map. In addition, masking was significantly inversely correlated to the speech reception performance difference between the overlap and the meet fitting. CONCLUSIONS: (1) This study confirmed the interaction between ipsilateral electric and acoustic stimulation in a psychoacoustic masking experiment. (2) The overlap fitting yielded poorer speech reception performance in stationary noise especially in subjects with strong masking. (3) The newly developed UNMASKfit strategy yielded similar speech reception thresholds with an enhanced acoustic benefit, while at the same time reducing the electric stimulation. This could be beneficial in the long-term if applied as a standard fitting, as hair cells are exposed to less possibly adverse electric stimulation. In this study, the UNMASKfit allowed the participants a better use of their natural hearing even after 1 month of adaptation. It might be feasible to transfer these results to the clinic, by fitting patients with the UNMASKfit upon their first fitting appointment, so that longer adaptation times can further improve speech reception.

Electric-acoustic interaction measurements in cochlear-implant users with ipsilateral residual hearing using electrocochleography.

Cochlear implantation is increasingly being used as a hearing-loss treatment for patients with residual hearing in the low acoustic frequencies. These patients obtain combined electric-acoustic stimulation (EAS). Substantial residual hearing and relatively long electrode arrays can lead to interactions between the electric and acoustic stimulation. This work investigated EAS interaction through psychophysical and electrophysiological measures. Moreover, cone-beam computed-tomography data was used to characterize the interaction along spatial cochlear locations. Psychophysical EAS interaction was estimated based on the threshold of audibility of an acoustic probe stimulus in the presence of a simultaneously presented electric masker stimulus. Intracochlear electrocochleography was used to estimate electrophysiological EAS interaction via the telemetry capability of the cochlear implant. EAS interaction was observed using psychophysical and electrophysiological measurements. While psychoacoustic EAS interaction was most pronounced close to the electrical stimulation site, electrophysiological EAS interaction was observed over a wider range of spatial cochlear locations. Psychophysical EAS interaction was significantly larger than electrophysiological EAS interaction for acoustic probes close to the electrode position.

Amplitude growth of intracochlear electrocochleography in cochlear implant users with residual hearing.

In cochlear implant (CI) users with residual hearing, the electrode-nerve interface can be investigated combining electric-acoustic stimulation (EAS) via electrocochleography (ECochG), a technique to record cochlear potentials evoked by acoustic stimulation. EAS interaction was shown in previous studies using psychoacoustic experiments. This work characterizes EAS interaction through psychophysical experiments and the amplitude growth of cochlear microphonics (CM) and auditory nerve neurophonics (ANN) derived from intracochlear ECochG recordings. Significant CM responses were recorded at psychoacoustic threshold levels. The mean difference between psychoacoustic and CM threshold was 17.5 dB. No significant ANN responses were recorded at the psychoacoustic threshold level. At the psychoacoustic most comfortable level, significant CM and ANN responses were recorded. In the presence of electrical stimulation, the psychoacoustic detection thresholds were elevated on average by 2.38 dB while the recorded CM amplitudes were attenuated on average by 1.15 dB. No significant differences in electrophysiological EAS interaction across acoustic stimulation levels were observed from CM recordings. The presence of psychophysical and electrophysiological EAS interaction demonstrates that some aspects of psychoacoustic EAS interaction can be measured via intracochlear ECochG.

Deep learning models to remix music for cochlear implant users.

The severe hearing loss problems that some people suffer can be treated by providing them with a surgically implanted electrical device called cochlear implant (CI). CI users struggle to perceive complex audio signals such as music; however, previous studies show that CI recipients find music more enjoyable when the vocals are enhanced with respect to the background music. In this manuscript source separation (SS) algorithms are used to remix pop songs by applying gain to the lead singing voice. This work uses deep convolutional auto-encoders, a deep recurrent neural network, a multilayer perceptron (MLP), and non-negative matrix factorization to be evaluated objectively and subjectively through two different perceptual experiments which involve normal hearing subjects and CI recipients. The evaluation assesses the relevance of the artifacts introduced by the SS algorithms considering their computation time, as this study aims at proposing one of the algorithms for real-time implementation. Results show that the MLP performs in a robust way throughout the tested data while providing levels of distortions and artifacts which are not perceived by CI users. Thus, an MLP is proposed to be implemented for real-time monaural audio SS to remix music for CI users.

Optimization of a Spectral Contrast Enhancement Algorithm for Cochlear Implants Based on a Vowel Identification Model.

Speech intelligibility achieved with cochlear implants (CIs) shows large variability across different users. One reason that can explain this variability is the CI user's individual electrode nerve interface which can impact the spectral resolution they can achieve. Spectral resolution has been reported to be related to vowel and consonant recognition in CI listeners. One measure of spectral resolution is the spectral modulation threshold (SMT), which is defined as the smallest detectable spectral contrast in a stimulus. In this study we hypothesize that an algorithm that improves SMT may improve vowel identification, and consequently produce an improvement in speech understanding for CIs. With this purpose we implemented an algorithm, termed spectral contrast enhancement (SCE) that emphasizes peaks with respect to valleys in the audio spectrum. This algorithm can be configured with a single parameter: the amount of spectral contrast enhancement entitled "SCE factor". We would like to investigate whether the "SCE factor" can be individualized to each CI user. With this purpose we used a vowel identification model to predict the performance produced by the SCE algorithm with different "SCE factors" in a vowel identification task.In five CI users the new algorithm has been evaluated using a SMT task and a vowel identification task. The tasks were performed for SCE factors of 0 (no enhancement), 2 and 4. In general it seems that increasing the SCE factor produces a decrease in performance in both the SMT threshold and vowel identification.

Spectral contrast enhancement improves speech intelligibility in noise for cochlear implants.

Spectral smearing causes, at least partially, that cochlear implant (CI) users require a higher signal-to-noise ratio to obtain the same speech intelligibility as normal hearing listeners. A spectral contrast enhancement (SCE) algorithm has been designed and evaluated as an additional feature for a standard CI strategy. The algorithm keeps the most prominent peaks within a speech signal constant while attenuating valleys in the spectrum. The goal is to partly compensate for the spectral smearing produced by the limited number of stimulation electrodes and the overlap of electrical fields produced in CIs. Twelve CI users were tested for their speech reception threshold (SRT) using the standard CI coding strategy with and without SCE. No significant differences in SRT were observed between conditions. However, an analysis of the electrical stimulation patterns shows a reduction in stimulation current when using SCE. In a second evaluation, 12 CI users were tested in a similar configuration of the SCE strategy with the stimulation being balanced between the SCE and the non-SCE variants such that the loudness perception delivered by the strategies was the same. Results show a significant improvement in SRT of 0.57 dB (p < 0.0005) for the SCE algorithm.

Remixing music using source separation algorithms to improve the musical experience of cochlear implant users.

Music perception remains rather poor for many Cochlear Implant (CI) users due to the users' deficient pitch perception. However, comprehensible vocals and simple music structures are well perceived by many CI users. In previous studies researchers re-mixed songs to make music more enjoyable for them, favoring the preferred music elements (vocals or beat) attenuating the others. However, mixing music requires the individually recorded tracks (multitracks) which are usually not accessible. To overcome this limitation, Source Separation (SS) techniques are proposed to estimate the multitracks. These estimated multitracks are further re-mixed to create more pleasant music for CI users. However, SS may introduce undesirable audible distortions and artifacts. Experiments conducted with CI users (N = 9) and normal hearing listeners (N = 9) show that CI users can have different mixing preferences than normal hearing listeners. Moreover, it is shown that CI users' mixing preferences are user dependent. It is also shown that SS methods can be successfully used to create preferred re-mixes although distortions and artifacts are present. Finally, CI users' preferences are used to propose a benchmark that defines the maximum acceptable levels of SS distortion and artifacts for two different mixes proposed by CI users.

Stimulating on multiple electrodes can improve temporal pitch perception.

OBJECTIVE: The objective of this study was to test if stimulating multiple electrodes can improve temporal pitch ranking performance at low and high stimulation rates. DESIGN: Temporal pitch cues are usually based on modifying the stimulation rate of the implant and thereby provide a continuum of pitches on a single electrode up to approximately 300 Hz. STUDY SAMPLE: Ten cochlear implant subjects were asked to pitch rank stimuli presented with direct electrical stimulation. The pulses were applied on one, three, six, or eleven electrodes. In one of the conditions the current amplitude of each pulse was randomly varied between 0 and 100%. Their frequency ranged from 100 up to 500 pps. RESULTS: Listeners showed the previously reported performance pattern in most conditions with very good performance at the lowest standard rates and deteriorating performance to near chance level at the highest rate tested. Performance with eleven electrodes was significantly better than performance with one electrode at 500 pps. CONCLUSION: Stimulating on multiple electrodes can improve temporal pitch perception.

Psychoacoustic and electroencephalographic responses to changes in amplitude modulation depth and frequency in relation to speech recognition in cochlear implantees.

Temporal envelope modulations (TEMs) are one of the most important features that cochlear implant (CI) users rely on to understand speech. Electroencephalographic assessment of TEM encoding could help clinicians to predict speech recognition more objectively, even in patients unable to provide active feedback. The acoustic change complex (ACC) and the auditory steady-state response (ASSR) evoked by low-frequency amplitude-modulated pulse trains can be used to assess TEM encoding with electrical stimulation of individual CI electrodes. In this study, we focused on amplitude modulation detection (AMD) and amplitude modulation frequency discrimination (AMFD) with stimulation of a basal versus an apical electrode. In twelve adult CI users, we (a) assessed behavioral AMFD thresholds and (b) recorded cortical auditory evoked potentials (CAEPs), AMD-ACC, AMFD-ACC, and ASSR in a combined 3-stimulus paradigm. We found that the electrophysiological responses were significantly higher for apical than for basal stimulation. Peak amplitudes of AMFD-ACC were small and (therefore) did not correlate with speech-in-noise recognition. We found significant correlations between speech-in-noise recognition and (a) behavioral AMFD thresholds and (b) AMD-ACC peak amplitudes. AMD and AMFD hold potential to develop a clinically applicable tool for assessing TEM encoding to predict speech recognition in CI users.

A Fused Deep Denoising Sound Coding Strategy for Bilateral Cochlear Implants.

Cochlear implants (CIs) provide a solution for individuals with severe sensorineural hearing loss to regain their hearing abilities. When someone experiences this form of hearing impairment in both ears, they may be equipped with two separate CI devices, which will typically further improve the CI benefits. This spatial hearing is particularly crucial when tackling the challenge of understanding speech in noisy environments, a common issue CI users face. Currently, extensive research is dedicated to developing algorithms that can autonomously filter out undesired background noises from desired speech signals. At present, some research focuses on achieving end-to-end denoising, either as an integral component of the initial CI signal processing or by fully integrating the denoising process into the CI sound coding strategy. This work is presented in the context of bilateral CI (BiCI) systems, where we propose a deep-learning-based bilateral speech enhancement model that shares information between both hearing sides. Specifically, we connect two monaural end-to-end deep denoising sound coding techniques through intermediary latent fusion layers. These layers amalgamate the latent representations generated by these techniques by multiplying them together, resulting in an enhanced ability to reduce noise and improve learning generalization. The objective instrumental results demonstrate that the proposed fused BiCI sound coding strategy achieves higher interaural coherence, superior noise reduction, and enhanced predicted speech intelligibility scores compared to the baseline methods. Furthermore, our speech-in-noise intelligibility results in BiCI users reveal that the deep denoising sound coding strategy can attain scores similar to those achieved in quiet conditions.

Remixing Preferences for Western Instrumental Classical Music of Bilateral Cochlear Implant Users.

For people with profound hearing loss, a cochlear implant (CI) is able to provide access to sounds that support speech perception. With current technology, most CI users obtain very good speech understanding in quiet listening environments. However, many CI users still struggle when listening to music. Efforts have been made to preprocess music for CI users and improve their music enjoyment. This work investigates potential modifications of instrumental music to make it more accessible for CI users. For this purpose, we used two datasets with varying complexity and containing individual tracks of instrumental music. The first dataset contained trios and it was newly created and synthesized for this study. The second dataset contained orchestral music with a large number of instruments. Bilateral CI users and normal hearing listeners were asked to remix the multitracks grouped into melody, bass, accompaniment, and percussion. Remixes could be performed in the amplitude, spatial, and spectral domains. Results showed that CI users preferred tracks being panned toward the right side, especially the percussion component. When CI users were grouped into frequent or occasional music listeners, significant differences in remixing preferences in all domains were observed.

A Computational Model of the Electrically or Acoustically Evoked Compound Action Potential in Cochlear Implant Users with Residual Hearing.

OBJECTIVE: In cochlear implant users with residual acoustic hearing, compound action potentials (CAPs) can be evoked by acoustic (aCAP) or electric (eCAP) stimulation and recorded through the electrodes of the implant. We propose a novel computational model to simulate aCAPs and eCAPs in humans, considering the interaction between combined electric-acoustic stimulation that occurs in the auditory nerve. METHODS: The model consists of three components: a 3D finite element method model of an implanted cochlea, a phenomenological single-neuron spiking model for electric-acoustic stimulation, and a physiological multi-compartment neuron model to simulate the individual nerve fiber contributions to the CAP. RESULTS: The CAP morphologies closely resembled those known from humans. The spread of excitation derived from eCAPs by varying the recording electrode along the cochlear implant electrode array was consistent with published human data. The predicted CAP amplitude growth functions largely resembled human data, with deviations in absolute CAP amplitudes for acoustic stimulation. The model reproduced the suppression of eCAPs by simultaneously presented acoustic tone bursts for different masker frequencies and probe stimulation electrodes. CONCLUSION: The proposed model can simulate CAP responses to electric, acoustic, or combined electric-acoustic stimulation. It considers the dependence on stimulation and recording sites in the cochlea, as well as the interaction between electric and acoustic stimulation in the auditory nerve. SIGNIFICANCE: The model enhances comprehension of CAPs and peripheral electric-acoustic interaction. It can be used in the future to investigate objective methods, such as hearing threshold assessment or estimation of neural health through aCAPs or eCAPs.

The temporal mismatch across listening sides affects cortical auditory evoked responses in normal hearing listeners and cochlear implant users with contralateral acoustic hearing.

Combining a cochlear implant with contralateral acoustic hearing typically enhances speech understanding, although this improvement varies among CI users and can lead to an interference effect. This variability may be associated with the effectiveness of the integration between electric and acoustic stimulation, which might be affected by the temporal mismatch between the two listening sides. Finding methods to compensate for the temporal mismatch might contribute to the optimal adjustment of bimodal devices and to improve hearing in CI users with contralateral acoustic hearing. The current study investigates cortical auditory evoked potentials (CAEPs) in normal hearing listeners (NH) and CI users with contralateral acoustic hearing. In NH, the amplitude of the N1 peak and the maximum phase locking value (PLV) were analyzed under monaural, binaural, and binaural temporally mismatched conditions. In CI users, CAEPs were measured when listening with CI only (CIS_only), acoustically only (AS_only) and with both sides together (CIS+AS). When listening with CIS+AS, various interaural delays were introduced between the electric and acoustic stimuli. In NH listeners, interaural temporal mismatch resulted in decreased N1 amplitude and PLV. Moreover, PLV is suggested as a more sensitive measure to investigate the integration of information between the two listening sides. CI users showed varied N1 latencies between the AS_only and CIS_only listening conditions, with increased N1 amplitude when the temporal mismatch was compensated. A tendency towards increased PLV was also observed, however, to a lesser extent than in NH listeners, suggesting a limited integration between electric and acoustic stimulation. This work highlights the potential of CAEPs measurement to investigate cortical processing of the information between two listening sides in NH and bimodal CI users.

Corrigendum: Selective attention decoding in bimodal cochlear implant users.

[This corrects the article DOI: 10.3389/fnins.2022.1057605.].

A Deep Denoising Sound Coding Strategy for Cochlear Implants.

Cochlear implants (CIs) have proven to be successful at restoring the sensation of hearing in people who suffer from profound sensorineural hearing loss. CI users generally achieve good speech understanding in quiet acoustic conditions. However, their ability to understand speech degrades drastically when background interfering noise is present. To address this problem, current CI systems are delivered with front-end speech enhancement modules that can aid the listener in noisy environments. However, these only perform well under certain noisy conditions, leaving quite some room for improvement in more challenging circumstances. In this work, we propose replacing the CI sound coding strategy with a deep neural network (DNN) that performs end-to-end speech denoising by taking the raw audio as input and providing a denoised electrodogram, i.e., the electrical stimulation patterns applied to the electrodes across time. We specifically introduce a DNN that emulates a common CI sound coding strategy, the advanced combination encoder (ACE). We refer to the proposed algorithm as 'Deep ACE'. Deep ACE is designed not only to accurately code the acoustic signals in the same way that ACE would but also to automatically remove unwanted interfering noises, without sacrificing processing latency. The model was optimized using a CI-specific loss function and evaluated using objective measures as well as listening tests in CI participants. Results show that, based on objective measures, the proposed model achieved higher scores when compared to the baseline algorithms. Also, the proposed deep learning-based sound coding strategy gave eight CI users the highest speech intelligibility scores.

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.