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Related Experiment Video

Updated: May 26, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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Real-time control of a hearing instrument with EEG-based attention decoding.

Jens Hjortkjær1,2, Daniel D E Wong3,4, Alessandro Catania1

  • 1Hearing Systems Section, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.

Journal of Neural Engineering
|February 25, 2025
PubMed
Summary

This study introduces neurosteering for hearing aids, using electroencephalography (EEG) to decode auditory attention. This allows hearing devices to selectively enhance desired speech in noisy environments, improving perception for hearing-impaired individuals.

Keywords:
BCIEEGattention decodinghearing

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Area of Science:

  • Neuroscience
  • Audiology
  • Biomedical Engineering

Background:

  • Hearing aids struggle to enhance speech perception in noisy environments.
  • Current hearing aids cannot fully leverage advanced speech separation without user attention data.
  • Decoding auditory attention using electroencephalography (EEG) offers a potential solution for selective sound enhancement.

Purpose of the Study:

  • To present a real-time brain-computer interface (BCI) system for neurosteering hearing aids.
  • To enable hearing instruments to selectively enhance attended sound sources based on EEG decoding.
  • To demonstrate the feasibility of real-time attention decoding for steering acoustic feedback.

Main Methods:

  • Developed a BCI system combining EEG attention decoding with a multi-microphone hardware platform.
  • Utilized a stimulus-response model with canonical correlation analysis for real-time EEG decoding.
  • Implemented low-latency real-time speech separation using spatial beamforming.

Main Results:

  • Demonstrated a system capable of real-time EEG-based auditory attention decoding.
  • Showcased the ability of the system to steer acoustic feedback of competing speech streams.
  • Provided case studies illustrating the application of the neurosteering technology.

Conclusions:

  • Neurosteering holds significant potential for improving hearing aid performance in complex acoustic environments.
  • The developed BCI system offers a promising approach for personalized hearing assistance.
  • Publicly available software facilitates further research and development in attention-based hearing aid technology.