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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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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...
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基于fNIRS对分类器和特征选择技术进行指纹触摸的比较研究.

Urooj Abid, Osama Zulfiqar, Hammad Nazeer

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
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    机器学习使用功能近红外光谱 (fNIRS) 信号准确地分类手指运动. 特性优化显著提高了分类准确性,基因算法 (GA) 和粒子群优化 (PSO) 为脑-计算机接口提供了最佳结果.

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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:

    • 功能近红外光谱 (fNIRS) 是一种非侵入性神经成像技术.
    • 分类精细运动运动对于大脑与计算机接口 (BCI) 的发展至关重要.
    • 现有的机器学习 (ML) 模型需要优化功能集以提高性能.

    研究的目的:

    • 使用ML算法对五个手指的动作进行分类.
    • 评估功能优化对分类性能的影响.
    • 为基于fNIRS的BCI应用程序确定最佳特征选择方法.

    主要方法:

    • 从20名参与者获得了fNIRS信号,他们执行了五种不同的手指动作.
    • 从fNIRS信号中提取了17个空间特征.
    • 使用的支持向量机 (SVM) 和极端梯度提升 (XGBoost) 分类器.
    • 使用遗传算法 (GA),群优化 (ACO) 和粒子群优化 (PSO) 来进行特征选择.

    主要成果:

    • 功能优化显著改善了ML分类性能.
    • 在功能选择方面,GA和PSO的表现优于ACO.
    • 在分类准确度方面,XGBoost超过了SVM.
    • 使用XGBoost.使用GA优化功能实现的最高准确率为94.94%.

    结论:

    • 特性选择对于提高神经成像中ML模型的效率和准确性至关重要.
    • 优化的分类管道可以提高BCI系统的性能.
    • 这项研究为BCI应用提供了有效的fNIRS信号分类的框架.