The aperiodic exponent of neural activity varies with vigilance state in mice and men.

Recently the 1/f signal of human electroencephalography has attracted attention, as it could potentially reveal a quantitative measure of neural excitation and inhibition in the brain, that may be relevant in a clinical setting. The purpose of this short article is to show that the 1/f signal depends on the vigilance state of the brain in both humans and mice. Therefore, proper labelling of the EEG signal is important as improper labelling may obscure disease-related changes in the 1/f signal.

EEG-based detection of the locus of auditory attention with convolutional neural networks.

In a multi-speaker scenario, the human auditory system is able to attend to one particular speaker of interest and ignore the others. It has been demonstrated that it is possible to use electroencephalography (EEG) signals to infer to which speaker someone is attending by relating the neural activity to the speech signals. However, classifying auditory attention within a short time interval remains the main challenge.

Linear versus deep learning methods for noisy speech separation for EEG-informed attention decoding.

Objective: A hearing aid's noise reduction algorithm cannot infer to which speaker the user intends to listen to. Auditory attention decoding (AAD) algorithms allow to infer this information from neural signals, which leads to the concept of neuro-steered hearing aids. We aim to evaluate and demonstrate the feasibility of AAD-supported speech enhancement in challenging noisy conditions based on electroencephalography recordings.

Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research.

A common problem in neural recordings is the low signal-to-noise ratio (SNR), particularly when using non-invasive techniques like magneto- or electroencephalography (M/EEG). To address this problem, experimental designs often include repeated trials, which are then averaged to improve the SNR or to infer statistics that can be used in the design of a denoising spatial filter. However, collecting enough repeated trials is often impractical and even impossible in some paradigms, while analyses on existing data sets may be hampered when these do not contain such repeated trials.

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

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 cannot be used to measure intelligibility of connected discourse. Therefore, we developed a new method, the self-assessed Békesy procedure, which is an adaptive procedure that uses intelligibility ratings to converge to a person's speech reception threshold.

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

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.

Adaptive attention-driven speech enhancement for EEG-informed hearing prostheses.

State-of-the-art hearing prostheses are equipped with acoustic noise reduction algorithms to improve speech intelligibility. Currently, one of the major challenges is to perform acoustic noise reduction in so-called cocktail party scenarios with multiple speakers, in particular because it is difficult-if not impossible-for the algorithm to determine which are the target speaker(s) that should be enhanced, and which speaker(s) should be treated as interfering sources. Recently, it has been shown that electroencephalography (EEG) can be used to perform auditory attention detection, i.

The effect of head-related filtering and ear-specific decoding bias on auditory attention detection.

Objective: We consider the problem of Auditory Attention Detection (AAD), where the goal is to detect which speaker a person is attending to, in a multi-speaker environment, based on neural activity. This work aims to analyze the influence of head-related filtering and ear-specific decoding on the performance of an AAD algorithm.

Approach: We recorded high-density EEG of 16 normal-hearing subjects as they listened to two speech streams while tasked to attend to the speaker in either their left or right ear.

Auditory-Inspired Speech Envelope Extraction Methods for Improved EEG-Based Auditory Attention Detection in a Cocktail Party Scenario.

This paper considers the auditory attention detection (AAD) paradigm, where the goal is to determine which of two simultaneous speakers a person is attending to. The paradigm relies on recordings of the listener's brain activity, e.g.