Significance: The human brain is a highly complex system with nonlinear, dynamic behavior. A majority of brain imaging studies employing functional near-infrared spectroscopy (fNIRS), however, have considered only the spatial domain and have ignored the temporal properties of fNIRS recordings. Methods capable of revealing nonlinearities in fNIRS recordings can provide new insights about how the brain functions. Aim: The temporal characteristics of fNIRS signals are explored by comprehensively investigating their fractal properties. Approach: Fractality of fNIRS signals is analyzed using scaled windowed variance (SWV), as well as using visibility graph (VG), a method which converts a given time series into a graph. Additionally, the fractality of fNIRS signals obtained under resting-state and task-based conditions is compared, and the application of fractality in differentiating brain states is demonstrated for the first time via various classification approaches. Results: Results from SWV analysis show the existence of high fractality in fNIRS recordings. It is shown that differences in the temporal characteristics of fNIRS signals related to task-based and resting-state conditions can be revealed via the VGs constructed for each case. Conclusions: fNIRS recordings, regardless of the experimental conditions, exhibit high fractality. Furthermore, VG-based metrics can be employed to differentiate rest and task-execution brain states.

中文翻译：

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关于功能性近红外光谱信号的分形性：分析和应用。

启示：人脑是一个高度复杂的系统，具有非线性的动态行为。但是，大多数使用功能性近红外光谱（fNIRS）进行的脑成像研究仅考虑了空间域，而忽略了fNIRS记录的时间特性。能够揭示fNIRS记录中非线性的方法可以提供有关大脑功能的新见解。目的：通过全面研究fNIRS信号的分形特性，探索其时间特征。方法：使用缩放的窗口方差（SWV）以及使用能见度图（VG）分析fNIRS信号的分形性，该方法将给定的时间序列转换为图。此外，比较了在静止状态和基于任务的条件下获得的fNIRS信号的分形性，分形在区分脑部状态中的应用首次通过各种分类方法得到了证明。结果：SWV分析的结果表明fNIRS记录中存在高分形性。结果表明，可以通过针对每种情况构建的VG揭示与基于任务和休息状态的条件相关的fNIRS信号的时间特性差异。结论：无论实验条件如何，fNIRS记录均显示出高分形性。此外，基于VG的指标可用于区分休息和任务执行的大脑状态。结果表明，可以通过针对每种情况构造的VG揭示与基于任务和静止状态的条件相关的fNIRS信号的时间特性差异。结论：无论实验条件如何，fNIRS记录均显示出高分形性。此外，基于VG的指标可用于区分休息和任务执行的大脑状态。结果表明，通过针对每种情况构建的VG，可以揭示与基于任务的状态和静止状态有关的fNIRS信号的时间特性差异。结论：无论实验条件如何，fNIRS记录均显示出高分形性。此外，基于VG的指标可用于区分休息和任务执行的大脑状态。