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HeteroEEG: A Dual-Branch Spatial-Spectral-Temporal Heterogeneous Graph Network for EEG Classification.

Zanhao Fu, Huaiyu Zhu, Ruohong Huan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 3, 2025
    PubMed
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    This study introduces HeteroEEG, a novel method for analyzing electroencephalogram (EEG) data by treating brain connectivity as a heterogeneous graph. HeteroEEG improves pain and emotion recognition by better capturing complex brain lobe interactions.

    Area of Science:

    • Neuroscience
    • Machine Learning
    • Signal Processing

    Background:

    • Electroencephalogram (EEG) analysis often uses graph-based methods due to complex electrode arrangements.
    • Current graph neural networks assume homogeneous connectivity, failing to capture distinct intra-lobe and inter-lobe brain functional connectivity differences.

    Purpose of the Study:

    • To propose HeteroEEG, the first approach to model EEG spatial information using heterogeneous graph reasoning.
    • To effectively decouple different brain lobe types and their functional connections for improved EEG classification.

    Main Methods:

    • HeteroEEG employs a dual-branch network architecture to process spatial, spectral, and temporal EEG features.
    • It utilizes heterogeneous graph construction to represent the distinct functional connectivity within and between cerebral cortex lobes.

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    Main Results:

    • HeteroEEG demonstrated superior performance in pain and emotion recognition tasks compared to existing state-of-the-art methods.
    • The study validates the effectiveness of heterogeneous graph reasoning for EEG classification.

    Conclusions:

    • HeteroEEG offers a more biologically plausible model for EEG analysis by incorporating heterogeneous graph structures.
    • This novel approach provides a foundation for future advancements in graph-based EEG classification network design.