ArticleFrontiers in neuroscience2019
A Tutorial on Auditory Attention Identification Methods.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Competing speech streams are simultaneously represented in the human cortex during attention switching.PLoS biology · 2026Article
- Neural Tracking of Sustained Attention, Attention Switching, and Natural Conversation in Audiovisual Environments Using Wearable EEG.The European journal of neuroscience · 2026Article
- MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps and Noisy Sentences.Scientific data · 2025Article
- Non-Invasive Brain Stimulation and Artificial Intelligence in Communication Neuroprosthetics: A Bidirectional Approach for Speech and Hearing Impairments.Brain sciences · 2025Article
- Speech Reception Threshold Estimation via EEG-Based Continuous Speech Envelope Reconstruction.The European journal of neuroscience · 2025Article
- Article
- A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments.IEEE access : practical innovations, open solutions · 2025Article
- Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Cognitive component of auditory attention to natural speech events.Frontiers in human neuroscience · 2024Article
- Improving auditory attention decoding by classifying intracranial responses to glimpsed and masked acoustic events.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Novel bias-reduced coherence measure for EEG-based speech tracking in listeners with hearing impairment.Frontiers in neuroscience · 2024Article
- Article
- Neural speech tracking and auditory attention decoding in everyday life.Frontiers in human neuroscience · 2024Article
- A Deep Learning Model for Correlation Analysis between Electroencephalography Signal and Speech Stimuli.Sensors (Basel, Switzerland) · 2023Article
- EEG alpha and pupil diameter reflect endogenous auditory attention switching and listening effort.The European journal of neuroscience · 2022Article
- Multi-Armed Bandits in Brain-Computer Interfaces.Frontiers in human neuroscience · 2022Review
- Decoding Object-Based Auditory Attention from Source-Reconstructed MEG Alpha Oscillations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Implementation of an Online Auditory Attention Detection Model with Electroencephalography in a Dichotomous Listening Experiment.Sensors (Basel, Switzerland) · 2021Article
- Effects of Hearing Aid Noise Reduction on Early and Late Cortical Representations of Competing Talkers in Noise.Frontiers in neuroscience · 2021Article
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4 authors.
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Abstract
Auditory attention identification methods attempt to identify the sound source of a listener's interest by analyzing measurements of electrophysiological data. We present a tutorial on the numerous techniques that have been developed in recent decades, and we present an overview of current trends in multivariate correlation-based and model-based learning frameworks. The focus is on the use of linear relations between electrophysiological and audio data. The way in which these relations are computed differs. For example, canonical correlation analysis (CCA) finds a linear subset of electrophysiological data that best correlates to audio data and a similar subset of audio data that best correlates to electrophysiological data. Model-based (encoding and decoding) approaches focus on either of these two sets. We investigate the similarities and differences between these linear model philosophies. We focus on (1) correlation-based approaches (CCA), (2) encoding/decoding models based on dense estimation, and (3) (adaptive) encoding/decoding models based on sparse estimation. The specific focus is on sparsity-driven adaptive encoding models and comparing the methodology in state-of-the-art models found in the auditory literature. Furthermore, we outline the main signal processing pipeline for how to identify the attended sound source in a cocktail party environment from the raw electrophysiological data with all the necessary steps, complemented with the necessary MATLAB code and the relevant references for each step. Our main aim is to compare the methodology of the available methods, and provide numerical illustrations to some of them to get a feeling for their potential. A thorough performance comparison is outside the scope of this tutorial.
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