Graph convolution network-based eeg signal analysis: a review

Hui Xiong

doi:10.1007/s11517-025-03295-0

2025-01-30

PRE

DOI：10.1007/s11517-025-03295-0

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随着人工智能技术的进步，越来越多有效的方法被用于识别和分类脑电图（EEG）信号，以应对医疗保健和脑机接口领域的挑战。本文综述了图卷积网络（GCN）技术在EEG信号分析中的应用及主要成就。通过全面检索已发表的文献，首次分模块讨论了GCN的当前研究现状。本文提出了方法的详尽分类以及对关键模块（如脑图谱构建、节点特征提取和GCN架构设计）的系统分析。此外，我们特别关注了几个与GCN相关的重要研究问题。本综述加深了对GCN在EEG信号分析领域未来潜力的理解。同时，为相关领域的研究人员梳理了几个有价值的发展方向，例如分析不同GCN层的适用性、构建面向任务的GCN模型以及提高对有限数据的适应性。

Keyword:

Graph convolution network

Electroencephalography

Deep learning

Graph neural network

Brain-computer interface

期刊

Medical and Biological Engineering and Computing

IF:

2.6

论文数:

311

被引数:

7.7K

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