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基于学习的运动工件在fNIRS中处理:一个迷你回顾

Yunyi Zhao1, Haiming Luo1, Jianan Chen1

  • 1HUB of Intelligent Neuro-Engineering, CREATe, IOMS, Division of Surgery and Interventional Science (DSIS), University College London, Stanmore, United Kingdom.

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PubMed
概括
此摘要是机器生成的。

本综述探讨了基于学习的方法,以删除功能近红外光谱 (fNIRS) 数据中的运动工件 (MA). 它提出了一个新的框架来评估MA校正质量,提高神经血管研究的可靠性.

关键词:
大脑 - 计算机接口深度学习是一种深度学习.评估矩阵是一个评估矩阵.在FNIRS中使用.机器学习是机器学习.运动文物 运动文物

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科学领域:

  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程
  • 信号处理 信号处理

背景情况:

  • 功能近红外光谱 (fNIRS) 对受体运动引起的运动工件 (MA) 很敏感,从而损害了数据完整性.
  • 传统的MA处理方法往往会降低fNIRS研究中的血液动力学反应的可靠性和统计能力.
  • 关于在fNIRS中移除MA的基于先进学习的方法的研究有限.

研究的目的:

  • 审查和分析现有的基于学习的fNIRS运动工件处理方法.
  • 为了确定基于学习的MA校正技术的当前格局的研究差距.
  • 提出一个新的框架来评估fNIRS数据中MA校正的质量.

主要方法:

  • 对315项研究进行了系统的文献搜索,确定了7项与fNIRS中基于学习的MA移除相关的研究.
  • 分析当前基于学习的MA校正策略及其局限性.
  • 拟议的评估框架的开发,包括信号和模型质量指标 (例如, ΔSNR,混矩阵, MSE).

主要成果:

  • 确定了一项有限但不断增长的关于基于学习的fNIRS中MA去除的研究.
  • 强调缺乏标准化指标来评估MA校正技术的有效性.
  • 提出了一个全面的框架来评估fNIRS中MA校正质量.

结论:

  • 基于学习的方法有望改善fNIRS中的MA校正,但需要进一步开发和标准化.
  • 拟议的评估框架可以加强对MA校正技术的评估,从而获得更可靠的fNIRS数据.
  • 这项工作旨在指导未来的研究,并促进在神经血管研究中应用先进的基于学习的方法.