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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Tissue derived extracellular vesicles advance from disease mechanisms to clinical application.

Discover nano·2026
Same author

Blue Rubber Bleb Nevus Syndrome.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same author

Reorganization of Human Brain Waves Across Diverse States of Consciousness.

bioRxiv : the preprint server for biology·2026
Same author

Safety and efficacy of Da Vinci robot-assisted atrial septal defect repair in patients with different body mass index levels: a single-center retrospective analysis.

Journal of cardiothoracic surgery·2026
Same author

Risk factors for rebleeding at different time points after TIPS in cirrhotic patients with AEVB: A case-control study.

Medicine·2026
Same author

A retrospective study of the efficacy and safety of rituximab biosimilar for the treatment of membranous nephropathy.

BMC pharmacology & toxicology·2026

Related Experiment Video

Updated: Jan 9, 2026

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

15.1K

GCANet: Enhancing EEG-based auditory attention decoding with temporal frequency GCN and cross attention mechanisms.

Rui Dai1, Yuan Liao2, Qiushi Han1

  • 1School of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Neuroscience
|November 30, 2025
PubMed
Summary

This study introduces GCANet, a novel model for auditory attention decoding (AAD) using electroencephalography (EEG) signals. GCANet improves accuracy by analyzing brain connectivity and EEG-audio interactions, offering insights into selective listening.

Keywords:
Auditory attention decodingCross-attention mechanismsElectroencephalographyGraph convolution networks

More Related Videos

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
09:37

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Published on: July 5, 2015

9.5K
The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram
06:14

The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram

Published on: October 10, 2025

449

Related Experiment Videos

Last Updated: Jan 9, 2026

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

15.1K
Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
09:37

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Published on: July 5, 2015

9.5K
The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram
06:14

The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram

Published on: October 10, 2025

449

Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Selective auditory attention, or the cocktail party effect, enables focus on target speakers amid noise.
  • Auditory attention decoding (AAD) aims to identify attended speakers from electroencephalography (EEG) signals.
  • Existing AAD methods often neglect the graph structure inherent in EEG data.

Purpose of the Study:

  • To propose GCANet, an end-to-end model that leverages graph convolutional networks and cross-attention for improved AAD.
  • To capture functional brain connectivity and enhance EEG-audio feature interactions.
  • To evaluate GCANet's performance on public datasets for cross-trial and cross-subject decoding.

Main Methods:

  • Developed GCANet, integrating a time-frequency graph convolutional network (TFGCN) for brain connectivity analysis.
  • Incorporated a cross-attention mechanism to dynamically fuse EEG and audio features.
  • Validated the model on KUL, DTU, and AVGC datasets using 1-second decision windows.

Main Results:

  • GCANet achieved high decoding accuracies: 92.2% (KUL), 83.2% (DTU), and 62.6% (AVGC) in cross-trial settings.
  • Cross-subject accuracies reached 75.1% (KUL), 57.1% (DTU), and 55.6% (AVGC).
  • Analysis revealed potential gaze-related confounds and highlighted frontal and temporal regions in EEG-audio interactions.

Conclusions:

  • GCANet significantly enhances auditory attention decoding accuracy by modeling brain connectivity and cross-modal interactions.
  • Findings suggest potential confounds in AAD related to visual cues and identify key brain regions involved.
  • The study provides valuable insights into cross-modal EEG-audio interactions and future AAD research.