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EEG Emotion Recognition Based on Self-attention Dynamic Graph Neural Networks.

Chao Li, Yong Sheng, Haishuai Wang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary

    This study introduces a novel brain network representation learning method using self-attention dynamic graph neural networks for improved electroencephalogram (EEG)-based emotion recognition. The proposed approach effectively captures dynamic spatio-temporal brain network evolution, outperforming existing methods.

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    Area of Science:

    • Neuroscience
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Electroencephalogram (EEG) signals are crucial for emotion recognition due to the central nervous system's role in emotional expression.
    • Current deep learning methods for EEG emotion recognition often focus on individual channel representations, potentially missing dynamic network interactions.

    Purpose of the Study:

    • To develop a more robust method for EEG-based emotion recognition by effectively learning long-term dynamic representations of brain functional networks.
    • To address the limitations of existing methods in capturing the spatio-temporal evolution of brain connectivity during emotional states.

    Main Methods:

    • Proposed a brain network representation learning method utilizing self-attention dynamic graph neural networks.
    • The method captures both spatial structure and temporal evolution characteristics of brain networks from EEG data.
    • Evaluated the approach on the AMIGOS dataset.

    Main Results:

    • The proposed self-attention dynamic graph neural network method demonstrated superior performance compared to state-of-the-art techniques.
    • Effectively learned dynamic spatio-temporal representations of brain functional connections.
    • Achieved improved accuracy in EEG-based emotion recognition.

    Conclusions:

    • Effective learning of dynamic brain network representations is key to advancing EEG-based emotion recognition.
    • The proposed method offers a promising approach for capturing complex brain dynamics related to emotional states.
    • This work advances the field of affective computing and brain-computer interfaces.