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Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design
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高密度功能近红外光谱和机器学习用于视觉感知量化量化.

Hongwei Xiao1, Zhao Li2, Yuting Zhou3

  • 1School of Automotive Engineering, Jilin University, Changchun 130022, China.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
概括

高密度功能近红外光谱 (HfNIRS) 通过监测血红蛋白变化,有效量化视觉感知. 机器学习算法将这些变化与任务性能相关联,证明了HfNIRS.

关键词:
功能性近红外光谱成像技术.信息理论信息理论机器学习是机器学习.统计 统计 统计 统计 统计视觉感知 视觉感知 视觉感知

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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
  • 人与计算机的交互

背景情况:

  • 可穿戴式传感器产生监控指标的关键数据.
  • 功能近红外光谱学 (fNIRS) 允许对人类视觉感知进行非侵入性监测.
  • 通过fNIRS量化视觉感知在工程中具有潜在的应用.

研究的目的:

  • 设计实验程序以诱导和量化视觉感知变化.
  • 通过高密度fNIRS (HfNIRS) 测量的血液动力学反应与视觉任务性能相关联.
  • 探索机器学习在分析HfNIRS数据中用于视觉感知评估中的应用.

主要方法:

  • 在模拟驾驶任务中使用HfNIRS记录总血红蛋白 (Hbt),血红蛋白 (Hb) 和氧化血红蛋白 (HbO2).
  • 使用道维度缩小 (相互信息),特征提取 (统计措施) 和K-最近邻居 (KNN) 来进行任务分类.
  • 与Hbt,Hb和HbO2波动相关联的视觉任务得分.

主要成果:

  • HfNIRS成功记录了与视觉感知改变相对应的血液动力学变化.
  • 在不同的视觉任务中,KNN算法实现了高分类准确性 (96.3%±1.99%).
  • 较高的视觉任务得分与Hbt,Hb和HbO2.2更显著的波动相关.

结论:

  • 显然,视觉感知的变化会引发Hbt,Hb和HbO2.2的变化.
  • 结合机器学习,HfNIRS提供了一种有效的方法来量化视觉感知.
  • 需要进一步的研究来完善HfNIRS信号与定量分析的视觉感知之间的数学关系.