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Cross-Subject EEG Emotion Recognition Using SSA-EMS Algorithm for Feature Extraction.

Yuan Lu1,2, Jingying Chen2

  • 1Normal College, Jimei University, Xiamen 361021, China.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Singular Spectrum Analysis (SSA) with Effect-Matched Spatial Filtering (EMS) framework for improved emotion recognition from EEG data. The SSA-EMS method achieves high accuracy in cross-subject emotion classification, demonstrating robust generalization.

Keywords:
EEGEMSSSAcross-subjectemotion recognition

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Electroencephalography (EEG) is crucial for understanding brain activity related to emotions.
  • Extracting reliable emotion-specific features from EEG across different subjects remains a challenge.
  • Existing methods often struggle with noise and individual variability in EEG signals.

Purpose of the Study:

  • To develop and validate a novel framework, Singular Spectrum Analysis with Effect-Matched Spatial Filtering (SSA-EMS), for optimizing cross-subject EEG-based emotion feature extraction.
  • To enhance the accuracy and generalization capability of emotion recognition systems using EEG data.
  • To combine the noise reduction strengths of SSA with the dynamic feature extraction capabilities of EMS.

Main Methods:

  • The proposed SSA-EMS framework integrates Singular Spectrum Analysis (SSA) for noise reduction and Effect-Matched Spatial Filtering (EMS) for dynamic feature extraction.
  • Experiments utilized the SEED dataset with "cross-subject sample combination" and "subject-independent" evaluation paradigms.
  • Random Forest (RF) and Support Vector Machine (SVM) classifiers were used for pairwise classification of positive, neutral, and negative emotional states.

Main Results:

  • The SSA-EMS framework achieved over 98% accuracy with RF classification across the full frequency band, outperforming single frequency bands.
  • In subject-independent evaluations, the model maintained accuracy above 96%, confirming strong cross-subject generalization.
  • The framework effectively captured dynamic neural differences associated with emotional states.

Conclusions:

  • The SSA-EMS framework represents a significant advancement in EEG-based emotion recognition, offering high accuracy and robust cross-subject generalization.
  • The integration of SSA and EMS effectively addresses noise and enhances the extraction of dynamic neural features.
  • Future research directions include exploring binary classification limitations and multimodal extensions for emotion recognition.