ArticleeLife2021
EEG-based detection of the locus of auditory attention with convolutional neural networks.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Real-time brain-controlled selective hearing enhances speech perception in multi-talker environments.Nature neuroscience · 2026Article
- Acoustic Scene-Aware Processing and Auditory Model-Based Compensation Strategies.Journal of the Association for Research in Otolaryngology : JARO · 2026Review
- Closing the Loop on the Cocktail Party Effect: From Attention Decoding to Neuro-Steered Selective Hearing.Biology · 2026Article
- Explainable AI Insights Into EEG Classification and Its Alignment to Neural Correlates.Human brain mapping · 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
- Spiking neural networks for EEG signal analysis using wavelet transform.Frontiers in neuroscience · 2025Article
- 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
- Who is WithMe? EEG features for attention in a visual task, with auditory and rhythmic support.Frontiers in neuroscience · 2024Article
- Auditory-GAN: deep learning framework for improved auditory spatial attention detection.PeerJ. Computer science · 2024Article
- Improving auditory attention decoding by classifying intracranial responses to glimpsed and masked acoustic events.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Envelope reconstruction of speech and music highlights stronger tracking of speech at low frequencies.PLoS computational biology · 2021Article
- EEG-based detection of the locus of auditory attention with convolutional neural networks.eLife · 2021Article
- Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline.PeerJ. Computer science · 2021Article
- Auditory Attention Detection via Cross-Modal Attention.Frontiers in neuroscience · 2021Article
- Extracting the Auditory Attention in a Dual-Speaker Scenario From EEG Using a Joint CNN-LSTM Model.Frontiers in physiology · 2021Article
- A Tutorial on Auditory Attention Identification Methods.Frontiers in neuroscience · 2019Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. We present a convolutional neural network-based approach to extract the locus of auditory attention (left/right) without knowledge of the speech envelopes. Our results show that it is possible to decode the locus of attention within 1-2 s, with a median accuracy of around 81%. These results are promising for neuro-steered noise suppression in hearing aids, in particular in scenarios where per-speaker envelopes are unavailable.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.