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相关概念视频

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

940
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
940

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相关实验视频

Updated: Jul 21, 2025

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
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一个孤立的CNN架构用于使用初始浸泡图像对指触任务的分类:一个功能近红外光谱研究.

Muhammad Umair Ali1, Amad Zafar1, Karam Dad Kallu2

  • 1Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea.

Bioengineering (Basel, Switzerland)
|July 29, 2023
PubMed
概括

这项研究使用卷积神经网络 (CNN) 来使用功能近红外光谱学 (fNIRS) 来分类指尖敲击任务. 一个22层的CNN实现了89.2%的准确性,显示初始血液动力学反应是有效的脑活动分析.

关键词:
深度学习是一种深度学习.设计的 HRF 的设计.在FNIRS中使用.最初的入初始的入运动皮层的运动皮层.神经元发射的发生.

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相关实验视频

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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
  • 机器学习 机器学习

背景情况:

  • 功能近红外光谱 (fNIRS) 是一种非侵入性神经成像技术.
  • 分析血液动力学反应 (HR) 对于理解大脑活动至关重要.
  • 分类微妙的运动任务需要先进的分析方法.

研究的目的:

  • 通过使用 fNIRS 数据,调查手指敲击任务的分类.
  • 评估不同卷积神经网络 (CNN) 架构的有效性.
  • 为了确定捕获血液动力学反应的最佳持续时间,以便准确分类.

主要方法:

  • 使用功能近红外光谱学 (fNIRS) 来捕捉手指敲击任务期间的大脑活动.
  • 开发并测试了具有不同层次的孤立卷积神经网络 (CNN) 模型 (16, 19, 22, 25).
  • 基于血液动力学反应 (0.5到4秒) 的初始入持续时间构建了功能性t图.

主要成果:

  • 22层隔离的CNN模型实现了最高的分类准确率89.2%.
  • 使用初始浸泡血液动力学反应进行分类,证明比延迟反应更有效.
  • 对于指和小指敲击任务,已经确定了明确的大脑活动模式.

结论:

  • 22层的CNN模型提供了一个有效的方法来分类fNIRS数据从运动任务.
  • 最初的血液动力学浸泡特征对于区分微妙的大脑活动具有高度的歧视性.
  • 基于fNIRS的初始血液动力学反应分析具有诊断大脑氧气交换异常的潜力.