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An adaptive decoupling learning system informed by the brain functional structure for EEG decoding.

Pengrui Li1, Maoqin Peng1, Haokai Zhang2

  • 1School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.

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|October 24, 2025
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Summary
This summary is machine-generated.

A new functional-structural adaptive decoupling learning framework (FS-AD) enhances electroencephalography (EEG) decoding by analyzing brain connectivity patterns. This approach improves the accuracy of decoding various brain states like fatigue and emotion.

Keywords:
Adaptive decouplingBrain decoding techniquesData-driven approachesEEGLearning systemRegional pathway

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Area of Science:

  • Neuroscience and Brain-Computer Interfaces
  • Computational Neuroscience
  • Machine Learning for Neuroimaging

Background:

  • Neuroscientific studies highlight regional brain connections and hemispheric asymmetry.
  • Brain connectivity influences electroencephalography (EEG) signal representation.
  • Developing data-driven methods to decode EEG features is crucial.

Purpose of the Study:

  • To present a functional-structural adaptive decoupling learning framework (FS-AD) for decoding EEG patterns.
  • To integrate local-global spatial representations informed by cognitive brain structure insights.
  • To enhance EEG decoding efficiency by excavating connectivity patterns across different brain states.

Main Methods:

  • Implemented a one-dimensional separable convolution module and local-domain attention for inter-channel interaction extraction.
  • Developed a global-local kernel-level fusion decoder (GKFD) to amalgamate local features.
  • Designed a cross-domain adaptive fusion decoder (CAFD) to identify optimal cross-domain pathways.

Main Results:

  • The FS-AD framework significantly outperformed existing methods in decoding EEG for fatigue, emotion, and motor imagery states.
  • Demonstrated variations in coupling strength among brain regional pathway connections.
  • Identified optimal regional pathways specific to distinct brain states.

Conclusions:

  • FS-AD effectively decodes latent EEG features by leveraging functional and structural brain information.
  • The study elucidates the relationship between brain regional connectivity and activity representation.
  • Contributes to the development of universal brain decoding methodologies.