Introduction: Past, Present and Potential Recording Evoked Potentials: Means to an End Frequency-Domain: Music of the Hemispheres Finding Sources: Forwards and Backwards Sounds to Charm the Brain Interpreting the Waveforms: Time and Uncertainty Electrocochleography: From Song to Synapse Auditory Brainstem Responses: Peaks Along the Way Middle Latency Responses - The Brain and the Brawn Auditory Steady State and Following Responses: Dancing to the Rhythms Late Auditory Evoked Potentials: Changing the Things Which Are Endogenous Auditory Evoked Potentials: Attention Must Be Paid Infant Hearing Assessment: Opening Ears Neurotology and Neurology: From Cochlea to Cortex Auditory Neuropathy: When Time is Broke Cochlear Implants: Body Electric Concluding Comments: Beginning to Live

Human Auditory Evoked Potentials

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Objective A listener's neural responses can be decoded to identify the speaker the person is attending to in a cocktail party environment Such auditory attention detection methods have the potential to provide noise suppression algorithms in hearing devices with information about the listener's attention A challenge is the effect of noise and other acoustic conditions that can reduce the attention detection accuracy Specifically, noise can impact the ability of the person to segregate the sound sources and perform selective attention, as well as the external signal processing necessary to decode the attention effectively The aim of this work is to systematically analyze the effect of noise level and speaker position on attention decoding accuracy Approach 28 subjects participated in the experiment Auditory stimuli consisted of stories narrated by different speakers from two different locations, along with surrounding multi-talker background babble EEG signals of the subjects were recorded while they focused on one story and ignored the other The strength of the babble noise as well as the spatial separation between the two speakers were varied between presentations Spatio-temporal decoders were trained for each subject, and applied to decode attention of the subjects from every 30 s segment of data Behavioral speech recognition thresholds were obtained for the different speaker separations Main results Both the background noise level and the angular separation between speakers affected attention decoding accuracy Remarkably, attention decoding performance was seen to increase with the inclusion of moderate background noise (versus no noise), while across the different noise conditions performance dropped significantly with increasing noise level We also observed that decoding accuracy improved with increasing speaker separation, exhibiting the advantage of spatial release from masking Furthermore, the effect of speaker separation on the decoding accuracy became stronger when the background noise level increased A significant correlation between speech intelligibility and attention decoding accuracy was found across conditions Significance This work shows how the background noise level and relative positions of competing talkers impact attention decoding accuracy It indicates in which circumstances a neuro-steered noise suppression system may need to operate, in function of acoustic conditions It also indicates the boundary conditions for the operation of EEG-based attention detection systems in neuro-steered hearing prostheses

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EEG-based auditory attention detection: boundary conditions for background noise and speaker positions.

We measured neural tracking of the speech envelope across the adult lifespan and found a supralinear increase in envelope tracking with age. Using a more ecologically valid approach than auditory s...

Evidence for enhanced neural tracking of the speech envelope underlying age-related speech-in-noise difficulties.

Continuous speech processing.

Predicting individual speech intelligibility from the cortical tracking of acoustic- and phonetic-level speech representations.

Neural envelope tracking as a measure of speech understanding in cochlear implant users.

Objective: When listening to speech, the brain tracks the speech envelope. It is possible to reconstruct this envelope from EEG recordings. However, in people who hear using a cochlear implant (CI), the artifacts caused by electrical stimulation of the auditory nerve contaminate the EEG. The objective of this study is to develop and validate a method for assessing the neural tracking of speech envelope in CI users.&#13; &#13; Approach: To obtain EEG recordings free of stimulus artifacts, the electrical stimulation is periodically interrupted. During these stimulation gaps, artifact-free EEG can be sampled and used to train a linear envelope decoder. EEG recordings obtained during audible and inaudible (i.e. sub-threshold) stimulation were used to characterize the artifacts and their influence on the envelope reconstruction.&#13; &#13; Main results: The present study demonstrates for the first time that neural tracking of the speech envelope can be measured in response to ongoing electrical stimulation. The responses were validated to be truly neural and not affected by stimulus artifact.&#13; &#13; Significance: Besides applications in audiology and neuroscience, the characterization and elimination of stimulus artifacts will enable future EEG studies involving continuous speech in CI users. Measures of neural tracking of the speech envelope reflect interesting properties of the listener's perception of speech, such as speech intelligibility or attentional state. Successful decoding of neural envelope tracking will open new possibilities to investigate the neural mechanisms of speech perception with a CI.

/pdf/neural-tracking-of-the-speech-envelope-in-cochlear-implant-3qnc3yj2tv.pdf

Neural tracking of the speech envelope in cochlear implant users.

Objective Measurement of the cortical tracking of continuous speech from electroencephalography (EEG) recordings using a forward model is an important tool in auditory neuroscience. Usually the stimulus is represented by its temporal envelope. Recently, the phonetic representation of speech was successfully introduced in English. We aim to show that the EEG prediction from phoneme-related speech features is possible in Dutch. The method requires a manual channel selection based on visual inspection or prior knowledge to obtain a summary measure of cortical tracking. We evaluate a method to (1) remove non-stimulus-related activity from the EEG signals to be predicted, and (2) automatically select the channels of interest. Approach Eighteen participants listened to a Flemish story, while their EEG was recorded. Subject-specific and grand-average temporal response functions were determined between the EEG activity in different frequency bands and several stimulus features: the envelope, spectrogram, phonemes, phonetic features or a combination. The temporal response functions were used to predict EEG from the stimulus, and the predicted was compared with the recorded EEG, yielding a measure of cortical tracking of stimulus features. A spatial filter was calculated based on the generalized eigenvalue decomposition (GEVD), and the effect on EEG prediction accuracy was determined. Main results A model including both low- and high-level speech representations was able to better predict the brain responses to the speech than a model only including low-level features. The inclusion of a GEVD-based spatial filter in the model increased the prediction accuracy of cortical responses to each speech feature at both single-subject (270% improvement) and group-level (310%). Significance We showed that the inclusion of acoustical and phonetic speech information and the addition of a data-driven spatial filter allow improved modelling of the relationship between the speech and its brain responses and offer an automatic channel selection.

/pdf/data-driven-spatial-filtering-for-improved-measurement-of-2grms6zjft.pdf

Data-driven spatial filtering for improved measurement of cortical tracking of multiple representations of speech.

In clinical practice and research, speech intelligibility is generally measured by instructing the participant to recall sentences. Although this is a reliable and highly repeatable measure, it can...

https://journals.sagepub.com/doi/pdf/10.1177/2331216518802702

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Papers

Predicting individual speech intelligibility from the cortical tracking of acoustic- and phonetic-level speech representations.

Neural envelope tracking as a measure of speech understanding in cochlear implant users.

Neural tracking of the speech envelope in cochlear implant users.

Data-driven spatial filtering for improved measurement of cortical tracking of multiple representations of speech.

The Self-Assessed Békesy Procedure: Validation of a Method to Measure Intelligibility of Connected Discourse.