量化头发和皮肤特征对信号质量的影响,并提出改善的实际建议
Meryem A Yücel1,2, Jessica E Anderson1,2, De'Ja Rogers1,2
1Neurophotonics Center, Boston University, Boston, MA 02215, USA.
bioRxiv : the preprint server for biology
|November 18, 2024
概括
功能近红外光谱 (fNIRS) 研究受到个体差异的影响. 这项研究量化了头发,皮肤和其他因素如何影响fNIRS信号质量,为更具包容性的神经影像提供了指导方针.
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
- 医学物理 医学物理
背景情况:
- 功能近红外光谱 (fNIRS) 是一种具有广泛研究和临床潜力的非侵入性神经成像技术.
- fNIRS信号质量容易受到生物变异的影响,包括头发和皮肤的特性,可能会在研究中引入偏差.
- 确保fNIRS研究的包容性需要解决这些影响不同人群信号采集的生物物理因素.
研究的目的:
- 量化个体生物物理特征对fNIRS信号质量的影响.
- 为提高fNIRS研究的包容性和数据质量提供基于证据的指导方针.
- 为开发更强大,更适用于全球的fNIRS硬件和方法提供信息.
主要方法:
- 在不同发型 (厚度,颜色),皮肤颜色,头部大小,性别和年龄的参与者中对fNIRS信号质量的系统分析.
- 与这些个体差异相关的光吸收和散射效应的定量评估.
- 制定优化fNIRS数据收集协议的建议.
主要成果:
- 毛发特征,皮肤颜色和头部尺寸对fNIRS信号噪声比有显著影响.
- 量化了这些因素可以改变光线透和信号检测的程度.
- 确定了需要仔细考虑不同参与者群体的特定参数.
结论:
- 个体生物物理差异对fNIRS研究的包容性和标准化构成了关键挑战.
- 为研究人员提供了可行的指导方针,以减轻这些影响,包括帽子/ optode 选择和头发管理.
- 这项工作有助于为更广泛的神经成像应用开发更公平的fNIRS技术.
相关概念视频
01:23Design Example
316
The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
316
01:18Aliasing
119
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
119
01:10Reconstruction of Signal using Interpolation
174
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
174
01:30Classification of Signals
403
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
403
01:22Upsampling
204
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
204