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    此摘要是机器生成的。

    图形神经网络 (GNN) 正在为各种应用推进EEG分类. 本综述对GNN方法进行了分类,发现光谱层和特定节点特征,如原始EEG信号是最常见的.

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    科学领域:

    • 神经科学是一个神经科学.
    • 计算机科学 计算机科学
    • 机器学习 机器学习

    背景情况:

    • 图形神经网络 (GNN) 正在获得电脑电图 (EEG) 分类的吸引力.
    • 应用包括情绪识别,运动图像和诊断神经系统疾病.

    研究的目的:

    • 系统地审查和分类现有的基于GNN的EEG分类方法.
    • 确定该领域的趋势和常用方法.

    主要方法:

    • 详尽的文献搜索GNN用于EEG分类.
    • 确定用于比较分析的方法的分类.

    主要成果:

    • 观察到光谱图的卷积层比空间层更为普遍.
    • 常见的节点特征包括原始EEG信号和差异.

    结论:

    • 总结了基于GNN的EEG分类的当前趋势.
    • 突出了未来的研究方向,如转移学习和交叉频率交互建模.