On the mechanism of response latencies in auditory nerve fibers

Despite the structural differences of the middle and inner ears, the latency pattern in auditory nerve fibers to an identical sound has been found similar across numerous species. Studies have shown the similarity in remarkable species with distinct cochleae or even without a basilar membrane. This stimulus-, neuron-, and species- independent similarity of latency cannot be simply explained by the concept of cochlear traveling waves that is generally accepted as the main cause of the neural latency pattern. An original concept of Fourier pattern is defined, intended to characterize a feature of temporal processing—specifically phase encoding—that is not readily apparent in more conventional analyses. The pattern is created by marking the first amplitude maximum for each sinusoid component of the stimulus, to encode phase information. The hypothesis is that the hearing organ serves as a running analyzer whose output reflects synchronization of auditory neural activity consistent with the Fourier pattern. A combined research of experimental, correlational and meta-analysis approaches is used to test the hypothesis. Manipulations included phase encoding and stimuli to test their effects on the predicted latency pattern. Animal studies in the literature using the same stimulus were then compared to determine the degree of relationship. The results show that each marking accounts for a large percentage of a corresponding peak latency in the peristimulus-time histogram. For each of the stimuli considered, the latency predicted by the Fourier pattern is highly correlated with the observed latency in the auditory nerve fiber of representative species. The results suggest that the hearing organ analyzes not only amplitude spectrum but also phase information in Fourier analysis, to distribute the specific spikes among auditory nerve fibers and within a single unit. This phase-encoding mechanism in Fourier analysis is proposed to be the common mechanism that, in the face of species differences in peripheral auditory hardware, accounts for the considerable similarities across species in their latency-by-frequency functions, in turn assuring optimal phase encoding across species. Also, the mechanism has the potential to improve phase encoding of cochlear implants

Auf einem menschlichen Gehörmodell basierende Elektrodenstimulationsstrategie für Cochleaimplantate

Cochleaimplantate (CI), verbunden mit einer professionellen Rehabilitation, haben mehreren hunderttausenden Hörgeschädigten die verbale Kommunikation wieder ermöglicht. Betrachtet man jedoch die Rehabilitationserfolge, so haben CI-Systeme inzwischen ihre Grenzen erreicht. Die Tatsache, dass die meisten CI-Träger nicht in der Lage sind, Musik zu genießen oder einer Konversation in geräuschvoller Umgebung zu folgen, zeigt, dass es noch Raum für Verbesserungen gibt.Diese Dissertation stellt die neue CI-Signalverarbeitungsstrategie Stimulation based on Auditory Modeling (SAM) vor, die vollständig auf einem Computermodell des menschlichen peripheren Hörsystems beruht.Im Rahmen der vorliegenden Arbeit wurde die SAM Strategie dreifach evaluiert: mit vereinfachten Wahrnehmungsmodellen von CI-Nutzern, mit fünf CI-Nutzern, und mit 27 Normalhörenden mittels eines akustischen Modells der CI-Wahrnehmung. Die Evaluationsergebnisse wurden stets mit Ergebnissen, die durch die Verwendung der Advanced Combination Encoder (ACE) Strategie ermittelt wurden, verglichen. ACE stellt die zurzeit verbreitetste Strategie dar. Erste Simulationen zeigten, dass die Sprachverständlichkeit mit SAM genauso gut wie mit ACE ist. Weiterhin lieferte SAM genauere binaurale Merkmale, was potentiell zu einer Verbesserung der Schallquellenlokalisierungfähigkeit führen kann. Die Simulationen zeigten ebenfalls einen erhöhten Anteil an zeitlichen Pitchinformationen, welche von SAM bereitgestellt wurden. Die Ergebnisse der nachfolgenden Pilotstudie mit fünf CI-Nutzern zeigten mehrere Vorteile von SAM auf. Erstens war eine signifikante Verbesserung der Tonhöhenunterscheidung bei Sinustönen und gesungenen Vokalen zu erkennen. Zweitens bestätigten CI-Nutzer, die kontralateral mit einem Hörgerät versorgt waren, eine natürlicheren Klangeindruck. Als ein sehr bedeutender Vorteil stellte sich drittens heraus, dass sich alle Testpersonen in sehr kurzer Zeit (ca. 10 bis 30 Minuten) an SAM gewöhnen konnten. Dies ist besonders wichtig, da typischerweise Wochen oder Monate nötig sind. Tests mit Normalhörenden lieferten weitere Nachweise für die verbesserte Tonhöhenunterscheidung mit SAM.Obwohl SAM noch keine marktreife Alternative ist, versucht sie den Weg für zukünftige Strategien, die auf Gehörmodellen beruhen, zu ebnen und ist somit ein erfolgversprechender Kandidat für weitere Forschungsarbeiten.Cochlear implants (CIs) combined with professional rehabilitation have enabled several hundreds of thousands of hearing-impaired individuals to re-enter the world of verbal communication. Though very successful, current CI systems seem to have reached their peak potential. The fact that most recipients claim not to enjoy listening to music and are not capable of carrying on a conversation in noisy or reverberative environments shows that there is still room for improvement.This dissertation presents a new cochlear implant signal processing strategy called Stimulation based on Auditory Modeling (SAM), which is completely based on a computational model of the human peripheral auditory system.SAM has been evaluated through simplified models of CI listeners, with five cochlear implant users, and with 27 normal-hearing subjects using an acoustic model of CI perception. Results have always been compared to those acquired using Advanced Combination Encoder (ACE), which is today’s most prevalent CI strategy. First simulations showed that speech intelligibility of CI users fitted with SAM should be just as good as that of CI listeners fitted with ACE. Furthermore, it has been shown that SAM provides more accurate binaural cues, which can potentially enhance the sound source localization ability of bilaterally fitted implantees. Simulations have also revealed an increased amount of temporal pitch information provided by SAM. The subsequent pilot study, which ran smoothly, revealed several benefits of using SAM. First, there was a significant improvement in pitch discrimination of pure tones and sung vowels. Second, CI users fitted with a contralateral hearing aid reported a more natural sound of both speech and music. Third, all subjects were accustomed to SAM in a very short period of time (in the order of 10 to 30 minutes), which is particularly important given that a successful CI strategy change typically takes weeks to months. An additional test with 27 normal-hearing listeners using an acoustic model of CI perception delivered further evidence for improved pitch discrimination ability with SAM as compared to ACE.Although SAM is not yet a market-ready alternative, it strives to pave the way for future strategies based on auditory models and it is a promising candidate for further research and investigation

Improvement of Speech Perception for Hearing-Impaired Listeners

Hearing impairment is becoming a prevalent health problem affecting 5% of world adult populations. Hearing aids and cochlear implant already play an essential role in helping patients over decades, but there are still several open problems that prevent them from providing the maximum benefits. Financial and discomfort reasons lead to only one of four patients choose to use hearing aids; Cochlear implant users always have trouble in understanding speech in a noisy environment. In this dissertation, we addressed the hearing aids limitations by proposing a new hearing aid signal processing system named Open-source Self-fitting Hearing Aids System (OS SF hearing aids). The proposed hearing aids system adopted the state-of-art digital signal processing technologies, combined with accurate hearing assessment and machine learning based self-fitting algorithm to further improve the speech perception and comfort for hearing aids users. Informal testing with hearing-impaired listeners showed that the testing results from the proposed system had less than 10 dB (by average) difference when compared with those results obtained from clinical audiometer. In addition, Sixteen-channel filter banks with adaptive differential microphone array provides up to six-dB SNR improvement in the noisy environment. Machine-learning based self-fitting algorithm provides more suitable hearing aids settings. To maximize cochlear implant users’ speech understanding in noise, the sequential (S) and parallel (P) coding strategies were proposed by integrating high-rate desynchronized pulse trains (DPT) in the continuous interleaved sampling (CIS) strategy. Ten participants with severe hearing loss participated in the two rounds cochlear implants testing. The testing results showed CIS-DPT-S strategy significantly improved (11%) the speech perception in background noise, while the CIS-DPT-P strategy had a significant improvement in both quiet (7%) and noisy (9%) environment

Coding Strategies for Cochlear Implants Under Adverse Environments

Cochlear implants are electronic prosthetic devices that restores partial hearing in patients with severe to profound hearing loss. Although most coding strategies have significantly improved the perception of speech in quite listening conditions, there remains limitations on speech perception under adverse environments such as in background noise, reverberation and band-limited channels, and we propose strategies that improve the intelligibility of speech transmitted over the telephone networks, reverberated speech and speech in the presence of background noise. For telephone processed speech, we propose to examine the effects of adding low-frequency and high- frequency information to the band-limited telephone speech. Four listening conditions were designed to simulate the receiving frequency characteristics of telephone handsets. Results indicated improvement in cochlear implant and bimodal listening when telephone speech was augmented with high frequency information and therefore this study provides support for design of algorithms to extend the bandwidth towards higher frequencies. The results also indicated added benefit from hearing aids for bimodal listeners in all four types of listening conditions. Speech understanding in acoustically reverberant environments is always a difficult task for hearing impaired listeners. Reverberated sounds consists of direct sound, early reflections and late reflections. Late reflections are known to be detrimental to speech intelligibility. In this study, we propose a reverberation suppression strategy based on spectral subtraction to suppress the reverberant energies from late reflections. Results from listening tests for two reverberant conditions (RT60 = 0.3s and 1.0s) indicated significant improvement when stimuli was processed with SS strategy. The proposed strategy operates with little to no prior information on the signal and the room characteristics and therefore, can potentially be implemented in real-time CI speech processors. For speech in background noise, we propose a mechanism underlying the contribution of harmonics to the benefit of electroacoustic stimulations in cochlear implants. The proposed strategy is based on harmonic modeling and uses synthesis driven approach to synthesize the harmonics in voiced segments of speech. Based on objective measures, results indicated improvement in speech quality. This study warrants further work into development of algorithms to regenerate harmonics of voiced segments in the presence of noise

A psychoacoustic "NofM"-type speech coding strategy for cochlear implants

We describe a new signal processing technique for cochlear implants using a psychoacoustic-masking model. The technique is based on the principle of a so-called "NofM" strategy. These strategies stimulate fewer channels (N) per cycle than active electrodes (NofM; N < M). In "NofM" strategies such as ACE or SPEAK, only the N channels with higher amplitudes are stimulated. The new strategy is based on the ACE strategy but uses a psychoacoustic-masking model in order to determine the essential components of any given audio signal. This new strategy was tested on device users in an acute Study, with either 4 or 8 channels stimulated per cycle. For the first condition (4 channels), the mean improvement over the ACE strategy was 17%. For the second condition (8 channels), no significant difference was found between the two strategies

InterlACE Sound Coding for Unilateral and Bilateral Cochlear Implants

Objective: Cochlear implant signal processing strategies define the rules of how acoustic signals are converted into electrical stimulation patterns. Technological and anatomical limitations, however, impose constraints on the signal transmission and the accurate excitation of the auditory nerve. Acoustic signals are degraded throughout cochlear implant processing, and electrical signal interactions at the electrode-neuron interface constrain spectral and temporal precision. In this work, we propose a novel InterlACE signal processing strategy to counteract the occurring limitations. Methods: By replacing the maxima selection of the Advanced Combination Encoder strategy with a method that defines spatially and temporally alternating channels, InterlACE can compensate for discarded signal content of the conventional processing. The strategy can be extended bilaterally by introducing synchronized timing and channel selection. InterlACE was explored unilaterally and bilaterally by assessing speech intelligibility and spectral resolution. Five experienced bilaterally implanted cochlear implant recipients participated in the Oldenburg Sentence Recognition Test in background noise and the spectral ripple discrimination task. Results: The introduced alternating channel selection methodology shows promising outcomes for speech intelligibility but could not indicate better spectral ripple discrimination. Conclusion: InterlACE processing positively affects speech intelligibility, increases available unilateral and bilateral signal content, and may potentially counteract signal interactions at the electrode-neuron interface. Significance: This work shows how cochlear implant channel selection can be modified and extended bilaterally. The clinical impact of the modifications needs to be explored with a larger sample size

Biophysical modeling of a cochlear implant system: progress on closed-loop design using a novel patient-specific evaluation platform

The modern cochlear implant is one of the most successful neural stimulation devices, which partially mimics the workings of the auditory periphery. In the last few decades it has created a paradigm shift in hearing restoration of the deaf population, which has led to more than 324,000 cochlear implant users today. Despite its great success there is great disparity in patient outcomes without clear understanding of the aetiology of this variance in implant performance. Furthermore speech recognition in adverse conditions or music appreciation is still not attainable with today's commercial technology. This motivates the research for the next generation of cochlear implants that takes advantage of recent developments in electronics, neuroscience, nanotechnology, micro-mechanics, polymer chemistry and molecular biology to deliver high fidelity sound. The main difficulties in determining the root of the problem in the cases where the cochlear implant does not perform well are two fold: first there is not a clear paradigm on how the electrical stimulation is perceived as sound by the brain, and second there is limited understanding on the plasticity effects, or learning, of the brain in response to electrical stimulation. These significant knowledge limitations impede the design of novel cochlear implant technologies, as the technical specifications that can lead to better performing implants remain undefined. The motivation of the work presented in this thesis is to compare and contrast the cochlear implant neural stimulation with the operation of the physiological healthy auditory periphery up to the level of the auditory nerve. As such design of novel cochlear implant systems can become feasible by gaining insight on the question `how well does a specific cochlear implant system approximate the healthy auditory periphery?' circumventing the necessity of complete understanding of the brain's comprehension of patterned electrical stimulation delivered from a generic cochlear implant device. A computational model, termed Digital Cochlea Stimulation and Evaluation Tool (‘DiCoStET’) has been developed to provide an objective estimate of cochlear implant performance based on neuronal activation measures, such as vector strength and average activation. A patient-specific cochlea 3D geometry is generated using a model derived by a single anatomical measurement from a patient, using non-invasive high resolution computed tomography (HRCT), and anatomically invariant human metrics and relations. Human measurements of the neuron route within the inner ear enable an innervation pattern to be modelled which joins the space from the organ of Corti to the spiral ganglion subsequently descending into the auditory nerve bundle. An electrode is inserted in the cochlea at a depth that is determined by the user of the tool. The geometric relation between the stimulation sites on the electrode and the spiral ganglion are used to estimate an activating function that will be unique for the specific patient's cochlear shape and electrode placement. This `transfer function', so to speak, between electrode and spiral ganglion serves as a `digital patient' for validating novel cochlear implant systems. The novel computational tool is intended for use by bioengineers, surgeons, audiologists and neuroscientists alike. In addition to ‘DiCoStET’ a second computational model is presented in this thesis aiming at enhancing the understanding of the physiological mechanisms of hearing, specifically the workings of the auditory synapse. The purpose of this model is to provide insight on the sound encoding mechanisms of the synapse. A hypothetical mechanism is suggested in the release of neurotransmitter vesicles that permits the auditory synapse to encode temporal patterns of sound separately from sound intensity. DiCoStET was used to examine the performance of two different types of filters used for spectral analysis in the cochlear implant system, the Gammatone type filter and the Butterworth type filter. The model outputs suggest that the Gammatone type filter performs better than the Butterworth type filter. Furthermore two stimulation strategies, the Continuous Interleaved Stimulation (CIS) and Asynchronous Interleaved Stimulation (AIS) have been compared. The estimated neuronal stimulation spatiotemporal patterns for each strategy suggest that the overall stimulation pattern is not greatly affected by the temporal sequence change. However the finer detail of neuronal activation is different between the two strategies, and when compared to healthy neuronal activation patterns the conjecture is made that the sequential stimulation of CIS hinders the transmission of sound fine structure information to the brain. The effect of the two models developed is the feasibility of collaborative work emanating from various disciplines; especially electrical engineering, auditory physiology and neuroscience for the development of novel cochlear implant systems. This is achieved by using the concept of a `digital patient' whose artificial neuronal activation is compared to a healthy scenario in a computationally efficient manner to allow practical simulation times.Open Acces