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在EEG分类中改进对象转移,通过差异估计进行差异估计.

Niklas Smedemark-Margulies1, Ye Wang2, Toshiaki Koike-Akino2

  • 1Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States of America.

Journal of neural engineering
|November 26, 2024
PubMed
概括
此摘要是机器生成的。

新的规范化技术提高了对未见的主体的脑电图 (EEG) 分类性能. 这些方法通过在培训期间强制执行统计关系来增强模型的概括性,从而减少了对用户特定校准的需求.

关键词:
大脑计算机接口 (BCI)域名适应 域名适应电脑电图 (EEG) 是一种电脑电图.代表性学习学习学习主题转移学习学习

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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
  • 信号处理 信号处理

背景情况:

  • 脑电图 (EEG) 分类模型通常在新受试者身上由于性能下降而失败.
  • 对于目前的EEG模型,受试者之间的高信号变化需要时间密集的校准.

研究的目的:

  • 通过使用新型规范化技术,提高对未见对象的EEG分类模型的性能.
  • 为了减少对EEG信号建模中的用户特定校准的需求.

主要方法:

  • 提出图形模型,以确定EEG数据中理想的统计关系.
  • 开发了使用相互信息和瓦瑟斯坦-1分歧来执行这些关系的规范化处罚.
  • 在使用二级神经网络进行模型训练时,对这些差异实施了高效的估计算法.

主要成果:

  • 对大型EEG数据集进行的广泛实验表明,与非规范化模型相比,其性能有了显著的改善.
  • 与各种超参数的基线对抗分类器相比,提出的技术显示出优异的性能和稳定性.
  • 拟议方法的计算成本与基线可比.

结论:

  • 新的规范化技术有效地提高了EEG分类的概括性,特别是在零射击主体转移场景中.
  • 这些进步有可能在EEG应用中显著减少或消除对用户特定模型校准的需求.