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适应性深度特征表示学习用于跨学科的EEG解码.

Shuang Liang1, Linzhe Li2, Wei Zu2

  • 1School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210093, China.

BMC bioinformatics
|December 31, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个自适应深度特征表示 (ADFR) 框架,以增强对不同受试者的脑电图 (EEG) 解码. 这种新的方法通过学习可转移的EEG特征来提高分类准确性,特别是在有限的数据中.

关键词:
学习的歧视性特征是学习.域名适应领域适应电脑脑电图 (EEG) 是一种电脑电图.输入量最小化最小化运动图像中的运动图像.

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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:

  • 收集大型脑电图 (EEG) 数据集以进行强大的模型训练是由于时间和劳动力限制而具有挑战性的.
  • 数据有限阻碍了EEG解码模型的通用性,需要诸如域调整之类的解决方案.
  • 目前用于EEG解码的域适应方法经常与剩余域转移发生冲突,导致错误分类.

研究的目的:

  • 提出一种新的自适应深度特征表示 (ADFR) 框架,以加强跨学科的EEG分类.
  • 提高EEG解码模型的通用性和准确性,特别是在低数据场景中.
  • 开发一种有效学习可转移的EEG特征表示的方法.

主要方法:

  • ADFR框架采用最大平均差异 (MMD) 规范化来最大限度地减少域分布差异.
  • 基于实例的歧视性特征学习 (IDFL) 规范化用于增强学习特征的歧视力.
  • 整合了最小化 (EM) 正规化,以完善分类器决策边界,通过协同学习提高性能.

主要成果:

  • 在两个基于公共汽车图像 (MI) 的EEG数据集 (BCI竞争III 4a和IV 2a) 上,ADFR框架证明了它的有效性.
  • 在各自的数据集上,ADFR比最先进的方法平均提高了3.0%和2.1%的准确性.
  • 结果表明,跨主题EEG分类性能显著提高.

结论:

  • 拟议的ADFR算法对EEG解码是有效的,在跨学科分类中显示出显著的改进.
  • 该框架显示了大脑计算机接口和其他基于EEG的技术的实际应用潜力.
  • 该研究强调了将域调整,区分特征学习和最小化结合起来,以实现强大的EEG解码的好处.