Wavelet-based method for removing global physiological noise in functional near-infrared spectroscopy
Wavelet-based method for removing global physiological noise in functional near-infrared spectroscopy
复制标题
基于小波的方法，用于消除功能近红外光谱中的全局生理噪声。
DOI：
10.1364/boe.9.003805
复制
发表时间：
2018-08-01
影响因子：
3.4
通讯作者：
Xu, Pengfei
中科院分区：
文献类型：
--
作者：
Duan, Lian;Zhao, Ziping;Xu, Pengfei
功能近红外光谱(FNIRS)是一种发展迅速的非侵入性脑功能成像技术，广泛应用于认知神经科学、临床研究和神经工程领域。然而，有效地去除fNIRS信号中的全局生理噪声是一个挑战。FNIR中的全局生理噪声来自于浅层组织和大脑的多个生理来源。它具有复杂的时间、空间和频率特征，对结果产生重大影响。在本研究中，我们发展了一种新的基于小波的fNIRS全局生理噪声去除方法。该方法是数据驱动的，并且不依赖于任何额外的硬件或主观噪声分量选择过程。它由两个步骤组成。首先，利用小波变换的相干性自动检测出被全局生理噪声污染的时频点。其次，利用小波变换对fNIRS信号进行分解，抑制受污染时频点的小波能量。最后，我们将信号转换回时间序列。我们使用任务状态和休息状态下的仿真数据和真实数据对该方法进行了验证。结果表明，该方法能有效去除fNIRS信号中的全局生理噪声，提高任务激活和静息态功能连接模式的空间特异性。(C)2018年OSA开放获取出版协议条款下的美国光学学会
Functional near-infrared spectroscopy (fNIRS) is a fast-developing non-invasive functional brain imaging technology widely used in cognitive neuroscience, clinical research and neural engineering. However, it is a challenge to effectively remove the global physiological noise in the fNIRS signal. The global physiological noise in fNIRS arises from multiple physiological origins in both superficial tissues and the brain. It has complex temporal, spatial and frequency characteristics, casting significant influence on the results. In the present study, we developed a novel wavelet-based method for fNIRS global physiological noise removal. The method is data-driven and does not rely on any additional hardware or subjective noise component selection procedure. It consists of two steps. Firstly, we use wavelet transform coherence to automatically detect the time-frequency points contaminated by the global physiological noise. Secondly, we decompose the fNIRS signal by using the wavelet transform, and then suppress the wavelet energy of the contaminated time-frequency points. Finally, we transform the signal back to a time series. We validated the method by using simulation and real data at both task- and resting-state. The results showed that our method can effectively remove the global physiological noise from the fNIRS signal and improve the spatial specificity of the task activation and the resting-state functional connectivity pattern. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement