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A multi-domain constraint learning system inspired by adaptive cognitive graphs for emotion recognition.

Dongrui Gao1, Mengwen Liu2, Haokai Zhang2

  • 1School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China; School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 12, 2025
PubMed
Summary

This study introduces a novel adaptive cognitive graph system (AC-DCL) for improved emotion recognition using electroencephalogram (EEG) data. The AC-DCL effectively captures stable cognitive functions from complex brain data, enhancing recognition performance.

Keywords:
Adaptive cognitive graphsEEGEmotion recognitionGraph constraint learningMulti-domain interactive attention

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Area of Science:

  • Neuroscience and Affective Computing
  • Machine Learning for Brain-Computer Interfaces

Background:

  • Electroencephalogram (EEG) analysis reveals brain's functional responses to emotions, but existing methods struggle with complex, domain-structured information due to over-reliance on cognitive priors.
  • Stable extraction of cognitive functions is vital for robust emotion recognition systems.

Purpose of the Study:

  • To propose a multi-domain constraint learning system (AC-DCL) inspired by adaptive cognitive graphs to enhance emotion recognition.
  • To overcome the limitations of cognitive prior dependence in existing approaches by adaptively generating and constraining functional relationships.

Main Methods:

  • Developed a spatial-guided dynamic graph constraint learning module to adaptively generate and constrain cognitive graph relationships.
  • Employed a temporal-driven sequence transformer to extract global temporal dependency features from EEG data.
  • Introduced a multi-domain interactive attention module for constraining domain-specific differences and aggregating complementary information.

Main Results:

  • The proposed AC-DCL system demonstrated significant advantages in emotion recognition across DREAMER, FACED, and SEED-IV datasets.
  • The system effectively captures stable cognitive functions from complex and dynamic cognitive structures.
  • The adaptive approach surpasses traditional static cross-domain interaction methods.

Conclusions:

  • The AC-DCL framework offers a powerful new approach for emotion recognition by leveraging spatiotemporal information and adaptive learning.
  • This method holds potential for advancing cross-domain interaction representation learning in neuroscience and BCI research.