Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 10, 2026

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
05:38

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

ASEAF: attention-SincNet driven EEG-audio fused target speaker extraction network.

Yuhang Yang1, Yuan Liao2, Qiushi Han1

  • 1College of electronic and optical engineering & college of flexible electronics (future technology), Nanjing University of Posts and Telecommunications, Jiangsu 210023, People's Republic of China.

Biomedical Physics & Engineering Express
|May 8, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Efficacy and Safety of Baxdrostat in the Management of Uncontrolled Hypertension: A Systematic Review and Meta-Analysis.

American journal of hypertension·2026
Same author

Where diverse populations gather: transit accessibility and the spatial structure of social mixing.

Frontiers in big data·2026
Same author

Pinocembrin inhibits melanoma progression via Ets1-mediated Smad3 transcription to enhance anti-PD-1 therapy.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Associations between fear of cancer recurrence and post-traumatic growth in patients with primary liver cancer: a latent profile analysis and mediation analysis.

Frontiers in psychiatry·2026
Same author

Automated in situ microfluidic Random-seq for robust single-nucleus and spatial total RNA profiling of diverse FFPE specimens.

Nature communications·2026
Same author

Targeting DNGR-1 with Fangchinoline Elevates Dendritic Cell Antigen Cross-Presentation-Mediated Antitumor Immunity in Melanoma.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

This study introduces ASEAF, an EEG-based model for extracting target speech in noise by fusing brain and audio signals. It significantly improves speech reconstruction for hearing aid solutions and brain-computer interfaces.

Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Selective auditory attention is challenging in noisy environments.
  • Existing methods struggle with precise target speaker extraction.
  • Neural decoding offers potential for advanced hearing assistance.

Purpose of the Study:

  • To develop an EEG-based model (ASEAF) for target speaker extraction in noisy conditions.
  • To improve speech reconstruction using simultaneous EEG and audio signal processing.
  • To enhance hearing aid solutions and brain-computer interfaces (BCIs).

Main Methods:

  • Utilized a Convolutional Neural Network (CNN) and self-attention for EEG spatio-temporal features.
  • Employed SincNet for frequency-aware audio feature extraction.
Keywords:
EEG-guidedSincNettarget speaker extractortransformer

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

Related Experiment Videos

Last Updated: May 10, 2026

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
05:38

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

  • Integrated a dual-path LSTM and CNN decoder for cross-modal fusion and waveform reconstruction.

    • Main Results:

    • ASEAF demonstrated superior performance over state-of-the-art models on multiple datasets.
    • Achieved an average improvement of 11.5% in scale-invariant signal-to-distortion ratio (SI-SDRi).
    • Showcased effective cross-modal interaction insights between EEG and audio signals.

    Conclusions:

    • ASEAF provides a robust solution for selective auditory attention in complex acoustic environments.
    • The model offers significant advancements for hearing aid technology.
    • This research contributes to the progress of brain-computer interfaces through neural signal processing.