ArticleScientific reports2019
Comparison of Two-Talker Attention Decoding from EEG with Nonlinear Neural Networks and Linear Methods.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Real-time brain-controlled selective hearing enhances speech perception in multi-talker environments.Nature neuroscience · 2026Article
- Decoding selective auditory attention to musical elements in ecologically valid music listening.Scientific reports · 2026Article
- Neural Tracking of Sustained Attention, Attention Switching, and Natural Conversation in Audiovisual Environments Using Wearable EEG.The European journal of neuroscience · 2026Article
- A direct comparison of simultaneously recorded scalp, around-ear and in-ear EEG for neural selective auditory attention decoding to speech.Scientific reports · 2025Article
- Hybrid channel attention network for auditory attention detection.Scientific reports · 2025Article
- Article
- A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments.IEEE access : practical innovations, open solutions · 2025Article
- Spiking neural networks for EEG signal analysis using wavelet transform.Frontiers in neuroscience · 2025Article
- Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks.Bioengineering (Basel, Switzerland) · 2024Article
- Brain-Controlled Augmented Hearing for Spatially Moving Conversations in Multi-Talker Environments.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention.PLoS computational biology · 2024Article
- A GRU-CNN model for auditory attention detection using microstate and recurrence quantification analysis.Scientific reports · 2024Article
- Improving auditory attention decoding by classifying intracranial responses to glimpsed and masked acoustic events.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Validation of cost-efficient EEG experimental setup for neural tracking in an auditory attention task.Scientific reports · 2023Article
- Affective Neural Responses Sonified through Labeled Correlation Alignment.Sensors (Basel, Switzerland) · 2023Article
- Decoding of the speech envelope from EEG using the VLAAI deep neural network.Scientific reports · 2023Article
- EEG alpha and pupil diameter reflect endogenous auditory attention switching and listening effort.The European journal of neuroscience · 2022Article
- Objective speech intelligibility prediction using a deep learning model with continuous speech-evoked cortical auditory responses.Frontiers in neuroscience · 2022Article
- EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks.Scientific reports · 2021Article
- Envelope reconstruction of speech and music highlights stronger tracking of speech at low frequencies.PLoS computational biology · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Auditory attention decoding (AAD) through a brain-computer interface has had a flowering of developments since it was first introduced by Mesgarani and Chang (2012) using electrocorticograph recordings. AAD has been pursued for its potential application to hearing-aid design in which an attention-guided algorithm selects, from multiple competing acoustic sources, which should be enhanced for the listener and which should be suppressed. Traditionally, researchers have separated the AAD problem into two stages: reconstruction of a representation of the attended audio from neural signals, followed by determining the similarity between the candidate audio streams and the reconstruction. Here, we compare the traditional two-stage approach with a novel neural-network architecture that subsumes the explicit similarity step. We compare this new architecture against linear and non-linear (neural-network) baselines using both wet and dry electroencephalogram (EEG) systems. Our results indicate that the new architecture outperforms the baseline linear stimulus-reconstruction method, improving decoding accuracy from 66% to 81% using wet EEG and from 59% to 87% for dry EEG. Also of note was the finding that the dry EEG system can deliver comparable or even better results than the wet, despite the latter having one third as many EEG channels as the former. The 11-subject, wet-electrode AAD dataset for two competing, co-located talkers, the 11-subject, dry-electrode AAD dataset, and our software are available for further validation, experimentation, and modification.
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