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EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations.

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    This study fuses brain activation and network patterns for better emotional recognition using electroencephalogram (EEG) data. Combining these features significantly improves accuracy compared to using either alone, advancing human-computer interaction.

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    Area of Science:

    • Neuroscience
    • Computer Science
    • Signal Processing

    Background:

    • Electroencephalogram (EEG) spectral power analysis is key for emotion recognition, reflecting brain region activity.
    • Emotions involve distinct large-scale brain networks and information processing patterns.
    • Current methods primarily focus on activation differences, potentially missing network dynamics.

    Purpose of the Study:

    • To improve EEG-based emotion recognition by fusing information propagation patterns with activation differences.
    • To explore how brain network characteristics contribute to emotional states.
    • To develop a more comprehensive approach for understanding brain activity during emotions.

    Main Methods:

    • Constructed emotion-related brain networks using phase locking value (PLV).
    • Employed a multiple feature fusion approach to integrate activation and network connection information.
    • Validated the method on three public emotional EEG databases.

    Main Results:

    • The fused features outperformed individual features based on power distribution or network characteristics.
    • Feature fusion analysis identified common patterns in activation and connectivity for positive, neutral, and negative emotions.
    • Demonstrated the superiority of the combined approach for robust emotion recognition.

    Conclusions:

    • The fusion of information propagation patterns and activation differences offers a significant advancement in EEG-based emotion recognition.
    • This integrated approach provides a more holistic understanding of brain activity related to emotions.
    • The findings are valuable for developing adaptive human-computer interaction systems capable of recognizing and responding to human emotions.