Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis
Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis
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DOI：
10.3390/app8010149
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发表时间：
2018-01-01
影响因子：
2.7
通讯作者：
Wada, Yasuhiro
中科院分区：
文献类型：
--
作者：
Oda, Yuta;Sato, Takanori;Wada, Yasuhiro
功能近红外光谱（fNIRS）是一种有效的非侵入性神经成像技术，用于测量大脑皮层中的血红蛋白浓度。由于fNIRS测量原理的性质，测量的信号可能被与任务相关的头皮血流量（SBF）污染，该信号分布在整个头部并掩盖了真正的大脑活动。针对基于fNIRS的实时应用，提出了一种实时任务相关的SBF伪影抑制方法。使用主成分分析，我们估计了全球的时间模式的SBF从几个短通道，然后我们应用了一个一般的线性模型，以消除它从长通道，可能被污染的SBF。滑动窗口分析应用于实时处理的两个信号步骤。为了评估性能，在运动任务实验中用测得的短通道信号执行半真实模拟。与没有SBF元素的传统技术相比，所提出的方法在任务相关的SBF伪影环境下显示出显着更高的真实脑激活估计性能。
Functional near-infrared spectroscopy (fNIRS) is an effective non-invasive neuroimaging technique for measuring hemoglobin concentration in the cerebral cortex. Owing to the nature of fNIRS measurement principles, measured signals can be contaminated with task-related scalp blood flow (SBF), which is distributed over the whole head and masks true brain activity. Aiming for fNIRS-based real-time application, we proposed a real-time task-related SBF artifact reduction method. Using a principal component analysis, we estimated a global temporal pattern of SBF from few short-channels, then we applied a general linear model for removing it from long-channels that were possibly contaminated by SBF. Sliding-window analysis was applied for both signal steps for real-time processing. To assess the performance, a semi-real simulation was executed with measured short-channel signals in a motor-task experiment. Compared with conventional techniques with no elements of SBF, the proposed method showed significantly higher estimation performance for true brain activation under a task-related SBF artifact environment.