基于功能近红外光谱的认知负荷血流动力学分解模型
A hemodynamic decomposition model for detecting cognitive load using functional near-infrared spectroscopy
摘要：
在本文中，我们介绍了一个参数化的数据驱动模型的功能近红外光谱，分解成一系列独立的，重新缩放，时移，血液动力学的基础功能的信号。每个分解的波形保留关于预期血液动力学行为的相关生物信息。该模型还提出了一个有效的迭代估计方法，以提高计算速度。我们的血液动力学分解模型（HDM）扩展了典型模型的情况下，当a）外部刺激是未知的，或b）当实验刺激和血液动力学反应之间的直接关系的假设不能成立。我们还认为，所提出的方法可以作为机器学习目的的特征变换方法。通过将我们设计的HDM应用于fNIRS信号的认知负荷分类任务，我们已经实现了86.20% ± 2.56%的准确性，使用额叶皮层中的六个通道，并且仅使用也位于额叶区域的AFpz通道，达到了86.34% ± 2.81%。相比之下，在相同的实验设置下，最先进的时间谱变换仅产生64.61%+-3.03%和37.8%+-2.96%。
英文摘要：
In the current paper, we introduce a parametric data-driven model for functional near-infrared spectroscopy that decomposes a signal into a series of independent, rescaled, time-shifted, hemodynamic basis functions. Each decomposed waveform retains relevant biological information about the expected hemodynamic behavior. The model is also presented along with an efficient iterative estimation method to improve the computational speed. Our hemodynamic decomposition model (HDM) extends the canonical model for instances when a) the external stimuli are unknown, or b) when the assumption of a direct relationship between the experimental stimuli and the hemodynamic responses cannot hold. We also argue that the proposed approach can be potentially adopted as a feature transformation method for machine learning purposes. By virtue of applying our devised HDM to a cognitive load classification task on fNIRS signals, we have achieved an accuracy of 86.20%+-2.56% using six channels in the frontal cortex, and 86.34%+-2.81% utilizing only the AFpz channel also located in the frontal area. In comparison, state-of-the-art time-spectral transformations only yield 64.61%+-3.03% and 37.8%+-2.96% under identical experimental settings.
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