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A Fine-grained Hemispheric Asymmetry Network for accurate and interpretable EEG-based emotion classification.

Ruofan Yan1, Na Lu2, Yuxuan Yan2

  • 1Systems Engineering Institute, School of Automation Science and Engineering, Xi'an Jiaotong University, People's Republic of China; Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region.

Neural Networks : the Official Journal of the International Neural Network Society
|January 14, 2025
PubMed
Summary

This study introduces the Fine-grained Hemispheric Asymmetry Network (FG-HANet) for accurate emotion classification using electroencephalography (EEG) data. The model reveals hemispheric dominance and asymmetry within specific frequency bands, offering new insights into emotion generation.

Keywords:
Brain signal analysisEEG emotion interpretabilityEmotion classification

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Signal Processing

Background:

  • Emotion classification from electroencephalography (EEG) data is crucial for understanding brain activity.
  • Leveraging hemispheric asymmetry in EEG signals offers a promising avenue for improved emotion recognition.
  • Existing methods often lack the fine-grained spectral analysis needed to capture subtle hemispheric differences.

Purpose of the Study:

  • To propose a novel deep learning model, the Fine-grained Hemispheric Asymmetry Network (FG-HANet), for accurate and interpretable emotion classification.
  • To utilize fine-grained hemispheric asymmetry features within narrow frequency bands from raw EEG data.
  • To investigate hemispheric dominance and asymmetry patterns during different emotional states.

Main Methods:

  • Developed an end-to-end deep learning model (FG-HANet) incorporating hemispheric asymmetry.
  • Extracted features from original and mirrored EEG inputs.
  • Applied Finite Impulse Response (FIR) filters with 2-Hz granularity for fine-grained spectral analysis.
  • Implemented a three-stage training pipeline to enhance attention to asymmetry features.

Main Results:

  • Achieved state-of-the-art accuracy on two public datasets (SEED: 97.11%, SEED-IV: 85.70%).
  • Demonstrated the model's superior performance in emotion classification.
  • Identified hemispheric dominance and asymmetry within 2-Hz frequency bands across individuals and emotional states.

Conclusions:

  • The FG-HANet model effectively leverages fine-grained hemispheric asymmetry for robust emotion classification.
  • Results align with and extend previous neuroscience findings on brain lateralization and emotion.
  • The study provides novel insights into the neural mechanisms of emotion generation through EEG analysis.