汉默斯坦-维纳运动工件校正用于功能近红外光谱:一种基于单位的新型惯性测量技术.
Hayder R Al-Omairi1,2, Arkan Al-Zubaidi1,3, Sebastian Fudickar4,5
1Applied Neurocognitive Psychology Lab, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
这项研究引入了一种新的非线性哈默斯坦-维纳模型,用于减少功能近红外光谱 (fNIRS) 脑活动测量中的运动工件. 该方法有效地消除了运动干扰,改善信号质量,以便进行可靠的分析.
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:
- 参与者运动在功能近红外光谱学 (fNIRS) 数据中引入了重要的文物.
- 运动工件 (MAs) 损害了fNIRS中大脑活动估计的准确性.
- 有效地缓解MA对于可靠的fNIRS研究至关重要.
研究的目的:
- 提出和评估一种新的非线性哈默斯坦-维纳模型,用于估计和减轻fNIRS信号中的运动工件.
- 用IMU传感器的运动数据,将拟议方法的性能与已建立的技术进行比较.
主要方法:
- 将非线性哈默斯坦-维纳模型应用于fNIRS信号,利用头部和探头上的惯性测量单元 (IMU) 传感器的数据.
- 在17名参与者中分析了血液动力学反应 (氧血红蛋白和脱氧血红蛋白),这些参与者执行了用不同头部动作进行手敲动的任务.
- 将新方法与八种现有方法 (PCA,tPCA,spline等) 相比较. 使用SNR,△AUC,RMSE和R指标.
主要成果:
- 与所有其他方法相比,非线性哈默斯坦-维纳方法显示信号与噪声比率 (SNR) 在统计学上显著增加 (p < 0.001).
- 视觉分析证实,通过拟议的方法,运动工件污染的减轻效果优越.
- 通过新方法获得的MA校正质量与直接使用头部IMU和探头IMU数据获得的质量相当.
结论:
- 非线性哈默斯坦-维纳模型为缓解fNIRS数据中的运动工件提供了一个高度有效的解决方案.
- 这种新的方法提高了从fNIRS获得的大脑活动估计的稳定性.
- 该方法为提高fNIRS研究的质量和可靠性提供了有价值的工具.
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