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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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深度学习驱动的电脑脑信号分析:推进神经学诊断

Jiahe Li, Xin Chen, Fanqi Shen

    IEEE reviews in biomedical engineering
    |December 9, 2025
    PubMed
    概括

    深度学习推进了使用脑电图 (EEG) 和脑内脑电图 (iEEG) 数据的神经诊断. 本综述强调了可扩展,可泛化的模型,并提出了可重现的大脑信号分析的基准.

    科学领域:

    • 神经科学是一个神经科学.
    • 人工智能的人工智能
    • 医学诊断 医学诊断 医学诊断

    背景情况:

    • 神经系统疾病对全球健康构成重大挑战.
    • 头皮脑电图 (EEG) 和内脑电图 (iEEG) 对于诊断和监测神经疾病至关重要.
    • 数据集的异质性和任务的可变性阻碍了用于大脑信号分析的强大深度学习模型的开发.

    研究的目的:

    • 系统地审查基于EEG/iEEG的神经诊断的近期深度学习进展.
    • 分析方法,性能,数据使用和模型设计,跨越7个神经疾病和46个数据集.
    • 突出预先训练的多任务模型的潜力,以提供可扩展和可泛化的解决方案.

    主要方法:

    • 对神经学诊断的深度学习方法的系统文献综述.
    • 分析了46个数据集,涵盖了7种不同的神经疾病.
    • 整合性能比较与数据使用,模型设计和特定任务的调整.

    主要成果:

    • 确定了各种神经疾病的关键深度学习方法及其定量结果.
    • 证明了预先训练的多任务模型在提高可扩展性和可通用性的作用.
    • 强调了最近的创新对智能和适应性神经健康系统的影响.

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    结论:

    • 深度学习显示了通过使用EEG/iEEG数据改善神经学诊断的显著前景.
    • 标准化基准对于评估模型性能和提高可重现性至关重要.
    • 未来的研究应该专注于开发适应性和可扩展性深度学习解决方案,用于各种神经疾病.