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EEG-Based Local-Global Dimensional Emotion Recognition Using Electrode Clusters, EEG Deformer, and Temporal

Hyoung-Gook Kim1, Jin-Young Kim2

  • 1Department of Electronic Convergence Engineering, Kwangwoon University, 20 Gwangun-ro, Nowon-gu, Seoul 01897, Republic of Korea.

Bioengineering (Basel, Switzerland)
|November 27, 2025
PubMed
Summary

This study introduces a novel brain-inspired framework using electroencephalography (EEG) clusters for dimensional emotion classification. The model effectively integrates local and global brain signals, improving accuracy in recognizing valence and arousal levels.

Keywords:
EEG deformerelectrode clusterselectroencephalographyemotion recognitiontemporal convolution network

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Area of Science:

  • Neuroscience
  • Affective Computing
  • Signal Processing

Background:

  • Emotions involve complex interactions across brain regions.
  • Electroencephalography (EEG) offers non-invasive neural activity monitoring.
  • Accurate dimensional emotion classification requires analyzing both local and global EEG signals.

Purpose of the Study:

  • To propose a brain-inspired EEG electrode-cluster-based framework for dimensional emotion classification.
  • To enhance the analysis of local electrode activity and global spatial distribution for improved emotion recognition.
  • To develop a scalable framework for affective computing and brain-computer interface (BCI) applications.

Main Methods:

  • Organized EEG electrodes into nine spatial and functional clusters.
  • Applied an EEG Deformer within each cluster to learn signal characteristics.
  • Integrated cluster features using bidirectional cross-attention (BCA) and temporal convolutional networks (TCN).
  • Utilized a multilayer perceptron (MLP) for valence and arousal classification.

Main Results:

  • The proposed cluster-based framework significantly outperformed existing EEG-based dimensional emotion recognition methods.
  • Demonstrated superior performance on three public EEG datasets.
  • Showcased effective capture of structural patterns at the electrode-cluster level and global signal interactions.

Conclusions:

  • Cluster-based learning enhances the interpretability and physiological validity of EEG-based dimensional emotion analysis.
  • The integration of inter-cluster information effectively models long-term dependencies.
  • The framework provides a robust and scalable approach for future affective computing and BCI research.