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Related Experiment Video

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SC-SDT: a framework with spectral convolution and spatial differential transformer for EEG-based emotion recognition.

Qiang Li1, Jiajin Huang1, Haiyan Zhou1

  • 1School of Information Science and Technology, Beijing University of Technology, Beijing, 100124 China.

Cognitive Neurodynamics
|December 29, 2025
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Summary

This study introduces SC-SDT, a novel framework for emotion recognition using electroencephalography (EEG) signals. It effectively extracts spectral-spatial features for improved objective emotional state quantification.

Keywords:
Attention mechanismConvolutionEEGEmotion recognitionSupervised contrastive learning

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Electroencephalography (EEG) signals offer objective emotional state quantification but extracting fine-grained features remains challenging.
  • Existing methods struggle to fully leverage both spectral and spatial information present in EEG data.
  • Inter-subject variability poses a significant hurdle for robust emotion recognition models.

Purpose of the Study:

  • To develop a novel framework, SC-SDT (Spectral Convolution-Spatial Differential Transformer), for enhanced EEG-based emotion recognition.
  • To jointly model spectral and spatial characteristics of EEG signals for improved feature extraction.
  • To enhance model robustness against inter-subject variability and achieve superior cross-subject generalization.

Main Methods:

  • Developed SC-SDT, integrating convolutional and transformer architectures for spectral-spatial feature modeling.
  • Employed a Spectral Feature Embedding module with sequential group-pointwise convolutions for dynamic spectral pattern capture.
  • Utilized a Spatial Feature Extraction module with a differential attention mechanism for optimized channel connectivity and noise reduction.
  • Incorporated supervised contrastive loss to enforce subject-invariant feature representations.

Main Results:

  • SC-SDT achieved competitive emotion classification performance on SEED, SEED-IV, and DEAP datasets under a subject-independent paradigm.
  • The framework effectively modeled spectral-spatial neural signatures for accurate emotion recognition.
  • Demonstrated superior cross-subject generalization capabilities, highlighting model robustness.

Conclusions:

  • SC-SDT offers a novel and effective approach for EEG-based emotion recognition by integrating spectral and spatial feature extraction.
  • The pioneering application of differential attention in EEG analysis contributes to mitigating attention noise and optimizing functional connectivity.
  • The proposed methodology provides a foundation for efficient spectral-spatial feature extraction, enhancing model performance and generalizability.