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EEG-based Emotion Recognition using Graph Attention Network with Dual-Branch Attention Module.

Cheng Li, Sio Hang Pun, Jia Wen Li

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces DAM-GAT, a novel graph attention network for EEG-based emotion recognition. The method achieves high accuracy by integrating dual-branch attention and phase-locking value connectivity for enhanced feature analysis.

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    Area of Science:

    • Neuroscience
    • Affective Computing
    • Machine Learning

    Background:

    • Electroencephalography (EEG) is crucial for understanding brain activity related to emotions.
    • Affective computing relies on accurate emotion recognition from physiological signals.
    • Existing methods for EEG-based emotion recognition have limitations in capturing complex signal features.

    Purpose of the Study:

    • To develop a novel approach for enhanced EEG-based emotion recognition.
    • To improve the accuracy and robustness of emotion detection from EEG signals.
    • To integrate advanced deep learning techniques with signal processing for affective computing.

    Main Methods:

    • Developed a Dual-Branch Attention Module (DAM) integrated into a Graph Attention Network (GAT).
    • Utilized GAT to capture local features within emotional EEG signals.
    • Incorporated DAM to weigh channel and frequency information, and employed Phase-Locking Value (PLV) for inter-channel connectivity analysis.

    Main Results:

    • The proposed DAM-GAT approach achieved a high accuracy of up to 94.63% on the SEED dataset.
    • Demonstrated superior performance compared to existing EEG-based emotion recognition methods.
    • The integration of DAM and PLV connectivity effectively enhanced the extraction of salient emotional features.

    Conclusions:

    • DAM-GAT represents a significant advancement in EEG-based emotion recognition.
    • The novel attention mechanism and connectivity analysis contribute to improved affective computing.
    • This method offers a promising direction for developing more sophisticated emotion-aware systems.