A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy
A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy
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DOI：
10.1016/j.neuroimage.2019.06.021
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发表时间：
2019-10-15
期刊：
影响因子：
5.7
通讯作者：
Adali, Tulay
中科院分区：
文献类型：
--
作者：
von Luehmann, Alexander;Boukouvalas, Zois;Adali, Tulay
在分析来自真实场景的功能性近红外光谱（fNIRS）信号时，伪影抑制至关重要。但目前并不存在金本位制。虽然大量的方法论方法隐含地假设信号中存在潜在的过程，但很少应用详细的盲源分离方法。一个原因是具有挑战性的特性，如非瞬时和非恒定耦合，相关噪声和信号分量之间的统计依赖性。我们提出了一种新的合适的BSS框架，该框架通过结合A）独立分量分析方法来解决这些问题，该方法利用高阶统计量和样本依赖性，B）多模态，即，具有加速度计信号的fNIRS，以及C）具有时间嵌入的典型相关分析。这使得能够分析信号分量并拒绝运动诱导的生理血流动力学伪影，否则将难以识别。本文提出了一种基于加速度计的盲源分离和干扰抑制检测方法（BLISSA（2）RD）。它允许分析一种新的基于n-back的认知工作负荷范式在自由移动的主题，这也是在这份手稿。我们利用基于一阶和二阶统计量以及SNR的度量对相应的数据集和模拟地面真实数据进行评估，并与三种已建立的方法进行比较：PCA、样条和基于小波的伪影去除。在17名受试者中，该方法被证明可以减少运动引起的伪影高达两个数量级，提高了单通道中连续血流动力学信号的SNR高达10 dB，并且在从严重污染的数据中提取模拟血流动力学响应函数方面显着优于传统方法。所提出的框架和方法可以作为一种新型的多变量方法的fNIRS信号分析的介绍，并作为一个蓝图，在复杂的环境中，超越了应用范例的伪影拒绝。
In the analysis of functional Near-Infrared Spectroscopy (fNIRS) signals from real-world scenarios, artifact rejection is essential. However, currently there exists no gold-standard. Although a plenitude of methodological approaches implicitly assume the presence of latent processes in the signals, elaborate Blind-Source-Separation methods have rarely been applied. A reason are challenging characteristics such as Non-instantaneous and non-constant coupling, correlated noise and statistical dependencies between signal components. We present a novel suitable BSS framework that tackles these issues by incorporating A) Independent Component Analysis methods that exploit both higher order statistics and sample dependency, B) multimodality, i.e., fNIRS with accelerometer signals, and C) Canonical-Correlation Analysis with temporal embedding. This enables analysis of signal components and rejection of motion-induced physiological hemodynamic artifacts that would otherwise be hard to identify. We implement a method for Blind Source Separation and Accelerometer based Artifact Rejection and Detection (BLISSA(2)RD). It allows the analysis of a novel n-back based cognitive workload paradigm in freely moving subjects, that is also presented in this manuscript. We evaluate on the corresponding data set and simulated ground truth data, making use of metrics based on 1st and 2nd order statistics and SNR and compare with three established methods: PCA, Spline and Wavelet-based artifact removal. Across 17 subjects, the method is shown to reduce movement induced artifacts by up to two orders of magnitude, improves the SNR of continuous hemodynamic signals in single channels by up to 10 dB, and significantly outperforms conventional methods in the extraction of simulated Hemodynamic Response Functions from strongly contaminated data. The framework and methods presented can serve as an introduction to a new type of multivariate methods for the analysis of fNIRS signals and as a blueprint for artifact rejection in complex environments beyond the applied paradigm.