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对欧几里德对齐与深度学习对EEG解码的系统评估.

Bruna Junqueira1,2, Bruno Aristimunha2,3, Sylvain Chevallier2

  • 1University of São Paulo, Sao Paulo, Brazil.

Journal of neural engineering
|May 22, 2024
PubMed
概括

欧几里德对齐 (EA) 增强了大脑计算机接口 (BCI) 的深度学习 (DL). 这种技术提高了解码精度,并大大减少了共享DL模型的训练时间,使BCI更有效率.

科学领域:

  • 神经科学是一个神经科学.
  • 机器学习 机器学习
  • 生物医学工程 生物医学工程

背景情况:

  • 电脑电图 (EEG) 信号对于脑计算机接口 (BCI) 任务至关重要.
  • 深度学习 (DL) 模型对BCI有希望,但需要大量的数据.
  • 使用多学科数据转移学习可以提高DL模型培训效率.

研究的目的:

  • 系统地评估欧几里德对齐 (EA) 对DL模型培训对BCI信号解码的影响.
  • 评估EA在改善BCI任务的共享和个人DL模型方面的有效性.
  • 调查EA在增强BCI应用中的转移学习绩效方面的作用.

主要方法:

  • 使用欧几里德对齐 (EA) 作为EEG数据的预处理步骤.
  • 训练共享DL模型,使用EA预处理的多主体数据.
  • 评估受过训练的模型对新主题的可转移性.
  • 将EA的性能与集成分类器中的单个DL模型进行了比较.

主要成果:

  • 通过EA预处理,目标对象的解码精度提高了4.33%.
  • 经过EA的研究,DL模型的趋同时间明显缩短了70%以上.
关键词:
大脑 计算机接口欧几里德对齐是什么意思神经网络的神经网络的神经网络

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  • 对于集体分类器,EA提高了3.71%的准确性,尽管与EA共享的模型仍然优于集体.

    • 结论:

    • 欧几里德对齐在增强BCI中DL模型的转移学习方面是有效的.
    • 电子分析可以成为一种有价值的预处理技术,以提高BCI的性能和效率.
    • 这些发现表明,EA可以成为BCI研究和应用的标准预处理方法.