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A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding.

Xin Xu1, Xinke Shen2, Xuyang Chen1

  • 1Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.

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|July 3, 2025
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Summary
This summary is machine-generated.

This study introduces the Multi-Context Emotional EEG (EmoEEG-MC) dataset, enabling better emotion decoding across different contexts. Findings show promising cross-context emotion recognition, advancing affective computing.

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Area of Science:

  • Neuroscience
  • Affective Computing
  • Psychology

Background:

  • EEG-based emotion decoding is crucial for understanding emotions and developing applications in mental health and human-machine interaction.
  • Current datasets lack multi-contextual data, limiting the generalizability of emotion decoding models.
  • The ability of emotion decoding to generalize across diverse elicitation contexts is largely unexplored.

Purpose of the Study:

  • To introduce the Multi-Context Emotional EEG (EmoEEG-MC) dataset, a novel resource for studying emotion decoding across different contexts.
  • To investigate the feasibility of cross-context emotion decoding using EEG and peripheral physiological data.
  • To provide a foundation for advancing affective computing and understanding the neural basis of emotion.

Main Methods:

  • Collected 64-channel EEG and peripheral physiological data from 60 participants under two emotion-elicitation contexts: video-induced and imagery-induced.
  • Evoked seven distinct emotional categories: joy, inspiration, tenderness, fear, disgust, sadness, and neutral.
  • Utilized a support vector machine with L1 regularization for cross-context emotion decoding analysis.

Main Results:

  • Achieved 66.7% accuracy for binary classification (positive vs. negative emotions) and 28.9% for seven-category emotion classification, both significantly above chance.
  • Demonstrated the potential for emotion decoding models to generalize across different elicitation contexts.
  • Validated emotional experiences through subjective reports.

Conclusions:

  • The EmoEEG-MC dataset is a valuable resource for advancing research in emotion decoding and affective computing.
  • Cross-context emotion decoding is feasible and shows potential for real-world applications.
  • This work contributes to a deeper understanding of the neural substrates of emotion across varied contexts.