A point process framework for modeling electrical stimulation of the auditory nerve

Model-based studies of auditory nerve responses to electrical stimulation can provide insight into the functioning of cochlear implants. Ideally, these studies can identify limitations in sound processing strategies and lead to improved methods for providing sound information to cochlear implant users. To accomplish this, models must accurately describe auditory nerve spiking while avoiding excessive complexity that would preclude large-scale simulations of populations of auditory nerve fibers and obscure insight into the mechanisms that influence neural encoding of sound information. In this spirit, we develop a point process model of the auditory nerve that provides a compact and accurate description of neural responses to electric stimulation. Inspired by the framework of generalized linear models, the proposed model consists of a cascade of linear and nonlinear stages. We show how each of these stages can be associated with biophysical mechanisms and related to models of neuronal dynamics. Moreover, we derive a semi-analytical procedure that uniquely determines each parameter in the model on the basis of fundamental statistics from recordings of single fiber responses to electric stimulation, including threshold, relative spread, jitter, and chronaxie. The model also accounts for refractory and summation effects that influence the responses of auditory nerve fibers to high pulse rate stimulation. Throughout, we compare model predictions to published physiological data and explain differences in auditory nerve responses to high and low pulse rate stimulation. We close by performing an ideal observer analysis of simulated spike trains in response to sinusoidally amplitude modulated stimuli and find that carrier pulse rate does not affect modulation detection thresholds.Comment: 1 title page, 27 manuscript pages, 14 figures, 1 table, 1 appendi

Spectral Grouping of Electrically Encoded Sound Predicts Speech-in-Noise Performance in Cochlear Implantees

\ua9 2023, The Author(s). Objectives: Cochlear implant (CI) users exhibit large variability in understanding speech in noise. Past work in CI users found that spectral and temporal resolution correlates with speech-in-noise ability, but a large portion of variance remains unexplained. Recent work on normal-hearing listeners showed that the ability to group temporally and spectrally coherent tones in a complex auditory scene predicts speech-in-noise ability independently of the audiogram, highlighting a central mechanism for auditory scene analysis that contributes to speech-in-noise. The current study examined whether the auditory grouping ability also contributes to speech-in-noise understanding in CI users. Design: Forty-seven post-lingually deafened CI users were tested with psychophysical measures of spectral and temporal resolution, a stochastic figure-ground task that depends on the detection of a figure by grouping multiple fixed frequency elements against a random background, and a sentence-in-noise measure. Multiple linear regression was used to predict sentence-in-noise performance from the other tasks. Results: No co-linearity was found between any predictor variables. All three predictors (spectral and temporal resolution plus the figure-ground task) exhibited significant contribution in the multiple linear regression model, indicating that the auditory grouping ability in a complex auditory scene explains a further proportion of variance in CI users’ speech-in-noise performance that was not explained by spectral and temporal resolution. Conclusion: Measures of cross-frequency grouping reflect an auditory cognitive mechanism that determines speech-in-noise understanding independently of cochlear function. Such measures are easily implemented clinically as predictors of CI success and suggest potential strategies for rehabilitation based on training with non-speech stimuli

Temporal Filterbanks in Cochlear Implant Hearing and Deep Learning Simulations

The masking phenomenon has been used to investigate cochlear excitation patterns and has even motivated audio coding formats for compression and speech processing. For example, cochlear implants rely on masking estimates to filter incoming sound signals onto an array. Historically, the critical band theory has been the mainstay of psychoacoustic theory. However, masked threshold shifts in cochlear implant users show a discrepancy between the observed critical bandwidths, suggesting separate roles for place location and temporal firing patterns. In this chapter, we will compare discrimination tasks in the spectral domain (e.g., power spectrum models) and the temporal domain (e.g., temporal envelope) to introduce new concepts such as profile analysis, temporal critical bands, and transition bandwidths. These recent findings violate the fundamental assumptions of the critical band theory and could explain why the masking curves of cochlear implant users display spatial and temporal characteristics that are quite unlike that of acoustic stimulation. To provide further insight, we also describe a novel analytic tool based on deep neural networks. This deep learning system can simulate many aspects of the auditory system, and will be used to compute the efficiency of spectral filterbanks (referred to as “FBANK”) and temporal filterbanks (referred to as “TBANK”)

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

Determination and evaluation of clinically efficient stopping criteria for the multiple auditory steady-state response technique

Background: Although the auditory steady-state response (ASSR) technique utilizes objective statistical detection algorithms to estimate behavioural hearing thresholds, the audiologist still has to decide when to terminate ASSR recordings introducing once more a certain degree of subjectivity. Aims: The present study aimed at establishing clinically efficient stopping criteria for a multiple 80-Hz ASSR system. Methods: In Experiment 1, data of 31 normal hearing subjects were analyzed off-line to propose stopping rules. Consequently, ASSR recordings will be stopped when (1) all 8 responses reach significance and significance can be maintained for 8 consecutive sweeps; (2) the mean noise levels were ≤ 4 nV (if at this “≤ 4-nV” criterion, p-values were between 0.05 and 0.1, measurements were extended only once by 8 sweeps); and (3) a maximum amount of 48 sweeps was attained. In Experiment 2, these stopping criteria were applied on 10 normal hearing and 10 hearing-impaired adults to asses the efficiency. Results: The application of these stopping rules resulted in ASSR threshold values that were comparable to other multiple-ASSR research with normal hearing and hearing-impaired adults. Furthermore, in 80% of the cases, ASSR thresholds could be obtained within a time-frame of 1 hour. Investigating the significant response-amplitudes of the hearing-impaired adults through cumulative curves indicated that probably a higher noise-stop criterion than “≤ 4 nV” can be used. Conclusions: The proposed stopping rules can be used in adults to determine accurate ASSR thresholds within an acceptable time-frame of about 1 hour. However, additional research with infants and adults with varying degrees and configurations of hearing loss is needed to optimize these criteria

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

The neural representation and behavioral detection of frequency modulation

Understanding a speech signal is reliant on the ability of the auditory system to accurately encode rapidly changing spectral and temporal cues over time. Evidence from behavioral studies in humans suggests that relatively poor temporal fine structure (TFS) encoding ability is correlated with poorer performance on speech understanding tasks in quiet and in noise. Electroencephalography, including measurement of the frequency-following response, has been used to assess the human central auditory nervous system’s ability to encode temporal patterns in steady-state and dynamic tonal stimuli and short syllables. To date, the FFR has been used to investigate the accuracy of phase-locked auditory encoding of various stimuli, however, no study has demonstrated an FFR evoked by dynamic TFS contained in the modulating frequency content of a carrier tone. Furthermore, the relationship between a physiological representation of TFS encoding and either behavioral perception or speech-in-noise understanding has not been studied. The present study investigated the feasibility of eliciting FFRs in young, normal-hearing listeners using frequency-modulated (FM) tones, which contain TFS. Brainstem responses were compared to the behavioral detection of frequency modulation as well as speech-in-noise understanding. FFRs in response to FM tones were obtained from all listeners, indicating a reliable measurement of TFS encoding within the brainstem. FFRs were more accurate at lower carrier frequencies and at shallower FM depths. FM detection ability was consistent with previously reported findings in normal-hearing listeners. In the present study, however, FFR accuracy was not predictive of behavioral performance. Additionally, FFR accuracy was not predictive of speech-in-noise understanding. Further investigation of brainstem encoding of TFS may reveal a stronger brain-behavior relationship across an age continuum

Spectral modulation detection in normal hearing children

This Capstone Project attempts to determine the ability of normal hearing children to resolve spectral information, and the relationship between spectral resolution ability and speech recognition ability in noise. This study also examines how these abilities develop with age