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Cross-Group EEG Emotion Recognition Based on Phase Space Reconstruction Topology.

Xuanpeng Zhu1, Mu Zhu1, Dong Li1

  • 1Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China.

Entropy (Basel, Switzerland)
|October 28, 2025
PubMed
Summary

This study introduces novel topological features for electroencephalogram (EEG) emotion recognition, achieving high accuracy in distinguishing emotions for both normal-hearing and hearing-impaired individuals.

Keywords:
cross-groupelectroencephalogram (EEG)emotion recognitionphase space reconstruction

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Electroencephalogram (EEG) signal nonlinearity and artifacts pose challenges for accurate emotion recognition.
  • Extracting meaningful features from complex EEG data requires advanced techniques.
  • Understanding emotional states through brain activity is crucial for various applications.

Purpose of the Study:

  • To develop a novel method for EEG emotion recognition by extracting topological features.
  • To address the challenges of signal nonlinearity and artifacts in EEG analysis.
  • To improve the accuracy and robustness of emotion recognition across different subject groups.

Main Methods:

  • Applied Local Linear Embedding (LLE) to reduce phase space trajectory dimensionality, preserving local topological structure.
  • Constructed 16 novel topological features to describe nonlinear dynamic patterns of emotions at multiple scales.
  • Utilized independent feature evaluation and brain topography analysis to select optimal features and electrode channels.

Main Results:

  • Achieved subject-dependent average accuracies of 90.33% (3-Class) for normal-hearing and 77.17% (4-Class) for hearing-impaired subjects on SEED and HIED datasets.
  • Demonstrated a 77.5% average accuracy for a 6-Class emotion recognition task across different subject groups by integrating topological features.
  • Showcased the effectiveness of topological features in capturing emotion-induced nonlinear dynamics in EEG signals.

Conclusions:

  • The proposed topological features offer a robust and effective approach for EEG-based emotion recognition.
  • The method shows promise for applications involving both normal-hearing and hearing-impaired populations.
  • Further integration of topological features with other signal processing techniques can enhance emotion recognition capabilities.