Real-time brain-controlled selective hearing enhances speech perception in multi-talker environments

Understanding speech in noisy environments is difficult for many people, and current hearing aids often fail because they amplify all sounds rather than the talker of interest. Auditory attention decoding (AAD) offers a potential solution by using the listener's brain signals to identify and enhance the attended speaker, but it has been unclear whether this can provide real-time perceptual benefits. Here we used high-resolution intracranial electroencephalography in patients undergoing neurosurgical procedures to implement a closed-loop system that achieves the decoding fidelity necessary to dynamically amplify the attended talker. Across multiple experiments, the system improved speech intelligibility, reduced listening effort and was consistently preferred by subjects. It also tracked both instructed and self-initiated attention shifts. By providing direct evidence that a real-time, brain-controlled hearing system can enhance perception, this work establishes a key performance benchmark for future auditory brain-computer interfaces and advances AAD from a theoretical concept to a validated solution for personalized assistive hearing.

Authors

Institutions

Publication Details

Journal
Nature Neuroscience
Published
2026-05-11
DOI
https://doi.org/10.1038/s41593-026-02281-5
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Real-time brain-controlled selective hearing enhances speech perception in multi-talker environments

Stephan Bickel, Nima Mesgarani, Vishal Choudhari, Maximilian Nentwich et al.
Nature Neuroscience
EEG and Brain-Computer Interfaces
article

Real-time brain-controlled selective hearing enhances speech perception in multi-talker environments

Stephan Bickel, Nima Mesgarani, Vishal Choudhari, Maximilian Nentwich, Adeen Flinker, Sarah Johnson, Ashesh D. Mehta, Edward F. Chang, Jose L. Herrero, Daniel Friedman
article en

Abstract

Understanding speech in noisy environments is difficult for many people, and current hearing aids often fail because they amplify all sounds rather than the talker of interest. Auditory attention decoding (AAD) offers a potential solution by using the listener's brain signals to identify and enhance the attended speaker, but it has been unclear whether this can provide real-time perceptual benefits. Here we used high-resolution intracranial electroencephalography in patients undergoing neurosurgical procedures to implement a closed-loop system that achieves the decoding fidelity necessary to dynamically amplify the attended talker. Across multiple experiments, the system improved speech intelligibility, reduced listening effort and was consistently preferred by subjects. It also tracked both instructed and self-initiated attention shifts. By providing direct evidence that a real-time, brain-controlled hearing system can enhance perception, this work establishes a key performance benchmark for future auditory brain-computer interfaces and advances AAD from a theoretical concept to a validated solution for personalized assistive hearing.

Nature Neuroscience
Feinstein Institute for Medical Research (US), Hofstra University (US), University of California, San Francisco (US), MIND Research Institute (US), Neurological Surgery (US), New York University (US), Columbia University (US)
National Institutes of Health, National Institute on Deafness and Other Communication Disorders
Quality Education
Openalex Percentile: Top 6%
EEG and Brain-Computer Interfaces
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.