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来自fNIRS数据的机器学习分类的基准测试框架.

Johann Benerradi1, Jeremie Clos1, Aleksandra Landowska1

  • 1School of Computer Science, University of Nottingham, Nottingham, United Kingdom.

Frontiers in neuroergonomics
|January 18, 2024
PubMed
概括
此摘要是机器生成的。

一个新的框架,BenchNIRS,为功能近红外光谱 (fNIRS) 大脑计算机接口标准化机器学习. 基准测试显示性能低于通常报告的水平,强调了大脑与计算机接口模型的概括性挑战.

关键词:
基准测试 (benchmarking) 是一种比较的方法.深度学习是一种深度学习.在FNIRS中使用.这些指导方针是指导方针.机器学习是机器学习.神经网络的神经网络的神经网络开放访问数据的数据

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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:

  • 缺乏用于功能近红外光谱 (fNIRS) 数据的标准化机器学习实践,阻碍了可靠的大脑与计算机接口 (BCI) 的开发.
  • 不一致的报告和缺乏开源基准使得评估BCI模型的通用性变得困难.

研究的目的:

  • 建立一个最佳实践,开源的基准测试框架 (BenchNIRS) 来评估应用于fNIRS数据的机器学习模型.
  • 使用fNIRS.提供一个标准化的方法来优化和评估BCI模型的性能.

主要方法:

  • 开发了BenchNIRS,这是一个开源框架,用于BCI的五个公共fNIRS数据集的嵌套交叉验证.
  • 与不同的训练数据和时间窗口大小进行对比,对六个机器学习模型 (LDA,SVM,kNN,ANN,CNN,LSTM) 进行了对比.
  • 研究了滑动窗与时代分类以及个性化与通用化方法之间的性能差异.

主要成果:

  • 对未见的数据的模型性能通常低于文献中报道的,模型之间的差异很小.
  • 基准分析强调了基于fNIRS的BCI实现高通用性的持续困难.
  • 该研究确定了影响分类性能的因素,包括数据数量和时间特征.

结论:

  • BenchNIRS为研究人员提供了一个标准化工具,用于严格评估和比较基于fNIRS的BCI的机器学习模型.
  • 这些发现强调了需要在fNIRS BCI研究中进行透明报告和强有力的验证.
  • 提供了关于方法和报告的建议,以利用fNIRS数据推进机器学习领域.