EEG-based Auditory Attention Decoding: Towards Neuro-Steered Hearing Devices

TL;DR

This paper reviews EEG-based auditory attention decoding algorithms and compares their performance, aiming to advance neuro-steered hearing devices for improved speech understanding in noisy environments.

Contribution

It provides a comprehensive review and a statistically grounded comparison of existing EEG-based AAD algorithms, highlighting key signal processing challenges.

Findings

Identifies main challenges in EEG-based AAD signal processing

Provides a comparative analysis of multiple AAD algorithms

Highlights potential for neuro-steered hearing devices

Abstract

People suffering from hearing impairment often have difficulties participating in conversations in so-called `cocktail party' scenarios with multiple people talking simultaneously. Although advanced algorithms exist to suppress background noise in these situations, a hearing device also needs information on which of these speakers the user actually aims to attend to. The correct (attended) speaker can then be enhanced using this information, and all other speakers can be treated as background noise. Recent neuroscientific advances have shown that it is possible to determine the focus of auditory attention from non-invasive neurorecording techniques, such as electroencephalography (EEG). Based on these new insights, a multitude of auditory attention decoding (AAD) algorithms have been proposed, which could, combined with the appropriate speaker separation algorithms and miniaturized EEG sensor devices, lead to so-called neuro-steered hearing devices. In this paper, we provide a broad review and a statistically grounded comparative study of EEG-based AAD algorithms and address the main signal processing challenges in this field.