The neural representation of an auditory spatial cue in the primate cortex.

Humans make use of small differences in the timing of sounds at the two ears-interaural time differences (ITDs)-to locate their sources. Despite extensive investigation, however, the neural representation of ITDs in the human brain is contentious, particularly the range of ITDs explicitly represented by dedicated neural detectors. Here, using magneto- and electro-encephalography (MEG and EEG), we demonstrate evidence of a sparse neural representation of ITDs in the human cortex.

Functional connectivity learning via Siamese-based SPD matrix representation of brain imaging data.

Measurement of brain functional connectivity has become a dominant approach to explore the interaction dynamics between brain regions of subjects under examination. Conventional functional connectivity measures largely originate from deterministic models on empirical analysis, usually demanding application-specific settings (e.g.

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures.

Disentangled and Side-Aware Unsupervised Domain Adaptation for Cross-Dataset Subjective Tinnitus Diagnosis.

EEG-based tinnitus classification is a valuable tool for tinnitus diagnosis, research, and treatments. Most current works are limited to a single dataset where data patterns are similar. But EEG signals are highly non-stationary, resulting in model's poor generalization to new users, sessions or datasets.

Side-Aware Meta-Learning for Cross-Dataset Listener Diagnosis With Subjective Tinnitus.

With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in EEG-based tinnitus analysis, most current models are dataset-specific. Each dataset may be limited to a specific range of symptoms, overall disease severity, and demographic attributes; further, dataset formats may differ, impacting model performance.

Cross-modal functional connectivity supports speech understanding in cochlear implant users.

Sensory deprivation can lead to cross-modal cortical changes, whereby sensory brain regions deprived of input may be recruited to perform atypical function. Enhanced cross-modal responses to visual stimuli observed in auditory cortex of postlingually deaf cochlear implant (CI) users are hypothesized to reflect increased activation of cortical language regions, but it is unclear if this cross-modal activity is "adaptive" or "mal-adaptive" for speech understanding. To determine if increased activation of language regions is correlated with better speech understanding in CI users, we assessed task-related activation and functional connectivity of auditory and visual cortices to auditory and visual speech and non-speech stimuli in CI users (n = 14) and normal-hearing listeners (n = 17) and used functional near-infrared spectroscopy to measure hemodynamic responses.

Helping People Hear Better with "Smart" Hearing Devices.

Millions of people around the world have difficulty hearing. Hearing aids and cochlear implants help people hear better, especially in quiet places. Unfortunately, these devices do not always help in noisy situations like busy classrooms or restaurants.

Deep reinforcement learning guided graph neural networks for brain network analysis.

Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted related methods for brain network analysis to be proposed.

Modulation Depth Discrimination by Cochlear Implant Users.

Cochlear implants (CIs) convey the amplitude envelope of speech by modulating high-rate pulse trains. However, not all of the envelope may be necessary to perceive amplitude modulations (AMs); the effective envelope depth may be limited by forward and backward masking from the envelope peaks. Three experiments used modulated pulse trains to measure which portions of the envelope can be effectively processed by CI users as a function of AM frequency.

Generalizable Sample-Efficient Siamese Autoencoder for Tinnitus Diagnosis in Listeners With Subjective Tinnitus.

Electroencephalogram (EEG)-based neurofeedback has been widely studied for tinnitus therapy in recent years. Most existing research relies on experts' cognitive prediction, and studies based on machine learning and deep learning are either data-hungry or not well generalizable to new subjects. In this paper, we propose a robust, data-efficient model for distinguishing tinnitus from the healthy state based on EEG-based tinnitus neurofeedback.

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers.

Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years.

Hidden Hearing Loss Impacts the Neural Representation of Speech in Background Noise.

Many individuals with seemingly normal hearing abilities struggle to understand speech in noisy backgrounds. To understand why this might be the case, we investigated the neural representation of speech in the auditory midbrain of gerbils with "hidden hearing loss" through noise exposure that increased hearing thresholds only temporarily. In noise-exposed animals, we observed significantly increased neural responses to speech stimuli, with a more pronounced increase at moderate than at high sound intensities.

Authenticity and Fashionable Disease in Eighteenth-Century Britain.

Throughout the eighteenth century the issue of authenticity shaped portrayals of fashionable diseases. From the very beginning of the century, writers satirized the behavior of elite invalids who paraded their delicacy as a sign of their status. As disorders such as the spleen came to be regarded as "fashionable," the legitimacy of patients' claims to suffer from distinguished diseases was called further into question, with some observers questioning the validity of the disease categories themselves.

Tolerable delay for speech production and perception: effects of hearing ability and experience with hearing aids.

Objective: Processing delay is one of the important factors that limit the development of novel algorithms for hearing devices. In this study, both normal-hearing listeners and listeners with hearing loss were tested for their tolerance of processing delay up to 50 ms using a real-time setup for own-voice and external-voice conditions based on linear processing to avoid confounding effects of time-dependent gain.

Design: Participants rated their perceived subjective annoyance for each condition on a 7-point Likert scale.

Influence of envelope waveform on ITD sensitivity of neurons in the auditory midbrain.

Interaural time differences (ITDs) conveyed by the modulated envelopes of high-frequency sounds can serve as a cue for localizing a sound source. Klein-Hennig et al. ( 129: 3856, 2011) demonstrated the envelope attack (the rate at which stimulus energy in the envelope increases) and the duration of the pause (the interval between successive envelope pulses) as important factors affecting sensitivity to envelope ITDs in human listeners.

Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners.

Machine-learning based approaches to speech enhancement have recently shown great promise for improving speech intelligibility for hearing-impaired listeners. Here, the performance of three machine-learning algorithms and one classical algorithm, Wiener filtering, was compared. Two algorithms based on neural networks were examined, one using a previously reported feature set and one using a feature set derived from an auditory model.

Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users.

Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the neural network to produce an estimation of which frequency channels contain more perceptually important information (higher signal-to-noise ratio, SNR).

A method to enhance the use of interaural time differences for cochlear implants in reverberant environments.

The ability of normal-hearing (NH) listeners to exploit interaural time difference (ITD) cues conveyed in the modulated envelopes of high-frequency sounds is poor compared to ITD cues transmitted in the temporal fine structure at low frequencies. Sensitivity to envelope ITDs is further degraded when envelopes become less steep, when modulation depth is reduced, and when envelopes become less similar between the ears, common factors when listening in reverberant environments. The vulnerability of envelope ITDs is particularly problematic for cochlear implant (CI) users, as they rely on information conveyed by slowly varying amplitude envelopes.

Sensitivity to Envelope Interaural Time Differences at High Modulation Rates.

Sensitivity to interaural time differences (ITDs) conveyed in the temporal fine structure of low-frequency tones and the modulated envelopes of high-frequency sounds are considered comparable, particularly for envelopes shaped to transmit similar fidelity of temporal information normally present for low-frequency sounds. Nevertheless, discrimination performance for envelope modulation rates above a few hundred Hertz is reported to be poor-to the point of discrimination thresholds being unattainable-compared with the much higher (>1,000 Hz) limit for low-frequency ITD sensitivity, suggesting the presence of a low-pass filter in the envelope domain. Further, performance for identical modulation rates appears to decline with increasing carrier frequency, supporting the view that the low-pass characteristics observed for envelope ITD processing is carrier-frequency dependent.

Factors affecting the use of envelope interaural time differences in reverberation.

At high frequencies, interaural time differences (ITDs) are conveyed by the sound envelope. Sensitivity to envelope ITDs depends crucially on the envelope shape. Reverberation degrades the envelope shape, reducing the modulation depth of the envelope and the slope of its flanks.

Development of social vocalizations in mice.

Adult mice are highly vocal animals, with both males and females vocalizing in same sex and cross sex social encounters. Mouse pups are also highly vocal, producing isolation vocalizations when they are cold or removed from the nest. This study examined patterns in the development of pup isolation vocalizations, and compared these to adult vocalizations.

A statistical, formant-pattern model for segregating vowel type and vocal-tract length in developmental formant data.

This paper investigates the theoretical basis for estimating vocal-tract length (VTL) from the formant frequencies of vowel sounds. A statistical inference model was developed to characterize the relationship between vowel type and VTL, on the one hand, and formant frequency and vocal cavity size, on the other. The model was applied to two well known developmental studies of formant frequency.